FIELD OF THE DISCLOSURE 
     This disclosure relates to automatic key frame extraction and storyboard interface generation for videos. 
     BACKGROUND 
     The use of video in reaching audiences has widened significantly in recent years, presenting a plethora of new video content as well as numerous ways to consume it. Efficiency in handling and visualizing such large amounts of video is becoming a vital prerequisite in the media sector, which may call for operational improvements in the creative and distribution processes behind video type media. 
     Within a video, a shot may refer to a video sequence captured from a unique camera perspective without cuts and/or other cinematic transitions. Improvements in navigation through video and content highlighting, such as user-selection of key frames or thumbnails for a given shot, may directly influence the time spent in production and/or content management. The selection and presentation of key frames of a video and/or thumbnails of the key frames, and/or other techniques for intuitive visualization of a video, may help to shape the viewing experience and may offer viewers a faster access to their desired content. For example, key frames and/or thumbnails, may be visually presented to a user in an interface including a one-dimensional timeline, often referred to as a “storyboard.” Individual key frames and/or thumbnails may be positioned at locations on the timeline corresponding to their temporal order in the video. The users may be able to select individually presented key frames to “jump” to a shot in the video corresponding to the selected key frame and/or thumbnail. 
     SUMMARY 
     One aspect of the disclosure relates to a system configured for automatic key frame extraction and storyboard interface generation for video. In some implementations, individual shots of a video may be determined. Individual shots may be grouped together based on feature similarity. A storyboard interface may be configured to display key frames of shots from the video. Key frames may be displayed in chronological order of the corresponding shots. To further assist a user producing and/or managing the video, key frames of grouped shots may be spatially correlated within the storyboard interface. For example, shots of a common group may be spatially correlated within a two-dimensional array so that they may be easily discernable as a group even though the shots may not be temporally consecutive and/or or even temporally close to each other in the timeframe of the video itself. By way of non-limiting example, in one or more implementations a storyboard interface may comprise a two-dimensional array, including a time-dependent dimension and a group-dependent dimension facilitating spatial correlation of key frames both temporally and based on groups. 
     In some implementations, the system may include one or more physical processors that are configured by machine-readable instructions. Executing the machine-readable instructions may cause the one or more physical processors to automatically extract key frames from video and generate a storyboard interface. The machine-readable instructions may include one or more of a shot component, a feature component, a group component, a key frame component, a storyboard component, and/or other components. 
     The shot component may be configured to obtain videos having multiple frames. The shot component may be configured to identify discrete shots in individual videos. A given shot of a given video may include consecutive frames of the video associated with a sequence that was captured from a unique camera perspective without cuts and/or other cinematic transitions. By way of non-limiting example, a first video having a first set of frames may be obtained. The shot component may be configured to identify a first shot, a second shot, and/or other shots of the first video. 
     The feature component may be configured to determine features of individual frames. By way of non-limiting example, features of an individual frame may include one or more of a size, position, and/or angle of individual faces depicted in the frame; a state of a mouth and/or eyes of a given face; a role assigned to a living being; text displayed; an image quality; a perspective of a video recording device capturing an image corresponding to a given frame; one or more actions that may be taking place; one or more background features and/or other characteristic feature points; and/or other features. By way of non-limiting example, actions may include one or more of explosions, car chases, and/or other action sequences. By way of non-limiting example feature may be detected by one or more feature detection techniques including one or more of SURF, SIFT, and/or other techniques. In some implementations, the feature component may be configured to determine a first feature and/or other features of a first frame of the first shot, a second feature and/or other features of a second frame of the second shot, and/or other features of other shots of the first video. 
     The group component may be configured to determine one or more groups of shots for individual videos based on the determined features. By way of non-limiting example, a given group may include one or more shots of a given video that share similar features. In some implementations, the group component may be configured such that the first shot may be assigned to a first group based on the first feature and/or other features of the first shot being similar to features of other shots assigned to the first group. The group component may be configured such that the second shot may be assigned to a second group based on the second feature and/or other features of the second shot being different from the first feature and/or other features of shots assigned to the first group. The group component may be configured to assign other shots of the first video to the first group, second group, and/or other groups. 
     The key frame component may be configured to select individual key frames representative of the individual shots. By way of non-limiting example, the key frame component may be configured such that the first frame may be selected as a first key frame of the first shot, the second frame may be selected as a second key frame of the second shot, and/or other frames of other shots of the first video may be selected as key frames of the other shots of the first video. 
     The storyboard component may be configured to effectuate presentation of the key frames in a storyboard interface. The storyboard interface may display the key frames in a chronological order of the corresponding shots. Key frames of grouped shots may be spatially correlated within the storyboard interface. By way of non-limiting example, the storyboard component may be configured to effectuate presentation of the first key frame, the second key frame, and/or other key frames of the first video in the first storyboard interface. The first key frame and/or other key frames of other shots assigned to the first group may be spatially correlated within the display of the first storyboard interface. 
     These and other features, and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a system configured for automatic key frame extraction and storyboard interface generation for video, in accordance with one or more implementations. 
         FIG. 2  illustrates an implementation of a server employed in the system of  FIG. 1 . 
         FIG. 3  illustrates a detection of text and/or face features displayed in an individual frame of a video, in accordance with one or more implementations. 
         FIG. 4  illustrates an exemplary storyboard interface corresponding to a video, in accordance with one or more implementations. 
         FIG. 5  illustrates a method of automatic key frame extraction and storyboard interface generation for video, in accordance with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a system  100  configured for automatic key frame extraction and storyboard interface generation for videos, in accordance with one or more implementations. A video may comprise a series of still images that may be presented in rapid succession to cause an appearance of movement of object, actors, and/or other elements portrayed in the images. A given image may correspond to a given frame of the video. A video may have multiple frames that individually correspond to an image, a sound track, and/or other components. A video may be a recorded video, a live video feed, and/or other audiovisual asset. 
     In some implementations, the system  100  may comprise a server  102 , one or more computing platforms  122 , and/or other components. The server  102  may include one or more physical processors  104  configured by machine-readable instructions  106  to automatically extract key frames from a video and/or generate a storyboard interface corresponding to that video. The machine-readable instructions  106  may include one or more of a shot component  108 , a feature component  110 , a group component  112 , a key frame component  114 , a storyboard component  116 , and/or other components. In some implementations, the server  102  may be configured to provide remote hosting of the features and/or function of the machine-readable instructions  106  to one or more computing platforms  122  that may be remotely located from the server  102 . In some implementations, one or more features and/or functions of server  102  may be attributed as local features and/or functions of one or more computing platforms  122 . By way of non-limiting example, individual ones of the computing platforms  122  may include machine-readable instructions comprising the same or similar components as machine-readable instructions  106  of server  102 . The computing platforms  122  may be configured to locally execute one or more components of the machine-readable instructions  106 . 
     The computing platforms  122  may include one or more of a cellular telephone, a smartphone, a laptop, a tablet computer, a desktop computer, a television set-top box, smart TV, a gaming console, and/or other computing platforms. 
     In some implementations, the shot component  108  may be configured to obtain videos, identify discrete shots in individual videos, and/or perform other functions. In some implementations, the shot component  108  may be configured to obtain video locally from server  102  (e.g., electronic storage  118 ), from one or more computing platforms  122  over network  120 , and/or from other sources. 
     In some implementations, a given shot of a given video may include consecutive frames of the video. The consecutive frames may be associated with a sequence that may have been captured from a unique camera perspective without cuts and/or other cinematic transitions. 
     In some implementations, identifying one or more discrete shots of a given video may be based on detecting shot boundaries and/or other techniques. Shot boundaries of a given shot may include one or more of a starting frame of the shot, an ending frame of the shot, and/or other frames associated with a given shot. In some implementations, the shot component  108  may be configured to determine starting and/or ending frames based on triggers in a video and/or other information. The shot component  108  may be configured to detect one or more triggers. A trigger may include one or more of a fade-in transition, a fade-out transition, an abrupt cut, a dissolve transition, and/or other trigger associated with a shot boundary. In some implementations, detecting triggers may be accomplished using image processing techniques such as comparing one or more frames within a given time window to determine an occurrence of significant changes (e.g., changes indicative of a trigger occurring), and/or other techniques configured to detect one or more triggers. By way of non-limiting example, detecting one or more change may be accomplished by using a histograms of color for individual frames, direct comparison of frames, detecting chaotic optical flow, and/or other techniques for detection changes that may be indicative of a trigger occurring. 
     In some implementations, the shot component  108  may be configured such that shots may be identified based on rank-tracing techniques and/or other techniques. By way of non-limiting example, rank-tracing may be accomplished by determining a histogram of frames of a video based on a hue-saturation-value (HSV) color space model of individual frames, a hue-saturation-lightness (HSL) color space model of individual frames, and/or based on other techniques for representing an RGB color model of a frame. 
     By way of non-limiting illustration in  FIG. 2 , the shot component  108  may be configured to obtain a first video  202  and/or other videos  206 . The first video  202  may have a first set of frames  204 . The shot component  108  may be configured to identify a first shot  208 , a second shot  210 , and/or other shots  212  of the first video  202 . By way of non-limiting example, the first shot  208  may comprise a first subset of the first set of frames  204 . By way of non-limiting example, the second shot  210  may comprise a second subset of the first set of frames  204 . The first subset may be different than the second subset. 
     The feature component  110  may be configured to determine features of individual frames. By way of non-limiting example, features of an individual frame may include one or more of a relative size, position, and/or angle of one or more individual faces depicted in the frame; a state of a mouth and/or eyes of a given face; text displayed; an image quality; a perspective of a video recording device capturing an image corresponding to a given frame (also referred to as camera pose); one or more actions that may be taking place; one or more background features and/or other characteristic feature points; and/or other features. By way of non-limiting example, actions may include one or more of explosions, car chases, and/or other action sequences. By way of non-limiting example, features may be detected by one or more feature detection techniques including one or more of SURF, SIFT, and/or other techniques. 
     In some implementations, the feature component  110  may be configured to detect one or more faces in individual frames and/or track individual faces over one or more frames of a given shot. Tracking may comprise correlating detections of text within different frames and/or other techniques. Face detection and/or tracking may be accomplished using object recognition, pattern recognition, searching for a specific pattern expected to be present in faces, and/or other image processing techniques. By way of non-limiting example, a specific pattern of a given face may include one or more of a predetermined positioning of eyes, nose, mouth, and/or other body parts, and/or other patterns. For example a predetermined positioning may comprise symmetric positioning. By way of non-limiting example, face detection and/or tracking may be accomplished using a sophisticated high-speed object recognition engine (SHORE), Viola-Jones object detection framework, and/or other techniques. 
     In some implementations, for individual detected faces, the feature component  110  may be configured to determine its relative position, size, and/or angle with respect to the camera; a state of the mouth and/or the eyes; an approximated age; a gender of the actor; a mood of the actor; and/or other features of a given face. Such determinations may be based on the SHORE technique, Viola-Jones object detection framework, and/or other techniques. A state of eyes may correspond to one or more of a whether the eyes are open, closed, winking, and/or in other states; a direction of gaze; and/or other state information. A state of a mouth may correspond to whether the mouth is open, closed, smiling, frowning, and/or in other state; a degree of “openness”; and/or other state information. 
     In some implementations, an image quality of a given frame may correspond to one or more of a presence of image artifacts, a resolution and/or blurriness measure, a palette of colors present, and/or other quality information. 
     In some implementations, the feature component  110  may be configured to determine which of the one or more detected faces may be classified as important in a given frame. In some implementations, importance may correspond to a person&#39;s role in the video and/or other measure of importance. A role may include one or a speaker, a listener, a primary actor, a secondary actor, a background actor, a temporary or transient actor, an audience member, and/or other roles. 
     In some implementations, an importance measure of a face may be determined based on various features of the given face, weighting placed on individual features of the face, and/or other information. In some implementations, one or more features of a face may include the determined relative position, size, and/or angle of a given face respect to the camera; the state of the mouth and the eyes; whether the face is detected over multiple frames; and/or other features. 
     By way of non-limiting example, a video may include one or more persons speaking, one or more persons listening, one or more persons spectating, and/or other features. A given speaker and/or a given listener may be depicted in a given frame as being positioned closer to the camera relative to one or more spectators. By way of non-limiting example, the spectators may be positioned in the background of a scene. As such, the given speaker and/or given listener may have face sizes that may be relatively larger than the face sizes of the one or more spectators (due to their positioning with respect to camera location). The feature component  110  may be configured to determine that the detected faces of the given speaker and/or listener may have a greater importance than the detected faces of the one or more spectators. 
     It is noted that the above example of a video including one or more persons speaking, one or more persons listening, and/or one or more persons spectating is not to be considered limiting. Instead, this is merely for illustrative purposes of describing an implementation wherein one or more features of detected faces (e.g., relative size) may correspond to a given importance classification of the given faces. It is not to be considered limiting with respect to how features are detected, the weighting assigned to those feature for classifying the faces, and/or how the faces may be classified. 
     In some implementations, a given face may be detected over multiple frames. Detected instances of a given face over multiple frames may be referred to collectively as a “family” of faces and/or other term. Family determination may facilitate providing a temporal coherence of the face detections over multiple frames. In some implementations, importance of a given face in a given frame may be determined based on importance scores assigned to the corresponding family of the given face and/or other information. Importance scores of a family may be determined based on individual feature score determined for individual detected faces in that family and/or overall family features such as a time duration of the presence of the family, and/or other features. In some implementations, if the number of families of detected faces of a given shot is below a given threshold, one or more of the faces associated with the families may initially and automatically be determined as important. After determining importance scores for families, the families with low score outliers may be determined as unimportant. In some implementations, if the number of families is above a threshold, one or more families with top score outliers may be determined as important while other ones of the families may be determined as unimportant. By way of non-limiting example, the following technique and/or other techniques for determining importance for individual families of detected faces may be used. 
     In some implementations, individual faces may be represented by f jk , where j represents the frame (e.g., a frame number) and k designates the individual face (e.g., a whole number starting from 1). Faces may be grouped into families per shot, represented as F i , where “i” designates the family (e.g., a whole number starting from 1). In some implementations, to determine an importance score per family, the following feature scores for a given family may be determined: a quality or flattery score Q(f jk ); a position score P(f jk ); a distance score D(f jk ); a size score M(f jk ), and/or other scores. In some implementations, the feature scores at the family level may be determined based on a mean and/or other measure of corresponding feature scores determined for individual faces of that family. 
     In some implementations, a quality or flattery score of a given face may be determined based on a relative angle of a face and/or head, a state of the eyes and/or mouth, a reliability factor, and/or other information. In some implementations, the position score may be determined based on a two-dimensional position of a given face within the image plane of the given frame. In some implementations, the distance score may be determined based on a mean distance of a given face to one or more other faces in the same frame. In some implementations, the size score may be determined based on a size of a given face relative to sizes of one or more other faces in the same frame. In some implementations, the individual feature scores of the individual faces of a given family may be normalized between 0 and 1, where 1 may represent a target or “best” score. The individual feature scores of individual faces that belong to the given family may be averaged to determine the features scores at the family level. 
     In some implementations, the final importance score, S(F i ), of a given family of faces may be calculated as: 
     
       
         
           
             
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     The term w i  may represent weights used to change the relative importance of the different scores. The total score S(F i ) may be normalized. Top and/or bottom outliers may be determined based on the normalized scores. 
     It is noted that the above description of feature scores and importance scores for individual face detections and/or for families of face detections is provided for illustrative purposes only and is not to be considered limiting. For example, the calculations of feature scores and/or importance scores is not to be considered limiting with respect to how faces may be detected, how roles may be assigned and/or determined for individual faces, how a face is determined as “important,” and/or how features of a given frame may be determined and/or evaluated. 
     In some implementations, the feature component  110  may be configured to detect text displayed in individual frames. In some implementations, text (e.g., a sentence and/or other text string) may be detected using text detection techniques and/or other techniques. By way of non-limiting example, a text detection technique may include Stroke Width Transform (SWT), high frequency analysis of the image including refinement stages based on machine learning, and/or other techniques. 
     In some implementations, a given string of text may be detected over multiple frames. The detected instances of a given text string over multiple frames may be referred to collectively as a “family” of text strings and/or other term. Family determination may facilitate providing a temporal coherence of the text detections over multiple frames. By way of non-limiting example, the following technique and/or other techniques for detecting text strings and/or determining text string families may be utilized. 
     In some implementations, “S” may denote a set of shots of a video, where S={S 1 , S 2 , . . . , S n }. Individual shots S i  may comprise a set of frames f, where S i ={f 1 , . . . , f m     i   }. For individual shots S i , a set of text strings B i  may be detected, wherein B={b jk :1≦j≦m i , k≧0}, and m i  is the number of the frames in a shot. A text string family may be denoted as T i . A text string family may contain the same or similar text strings detected across one or more frames of a shot. In some implementations, a given text string may not be part of more than one family. In some implementations, a given family may not be associated with more than one text string detected in a given frame. Text stings may be assigned to a given family based on one or more of similar spatial position within consecutive frames, size within consecutive frames, and/or other factor for assigning text strings to a given family. By way of non-limiting example, if a given text string b jk  is assigned to a given family T i , then one or more other given text strings b j′k′  with a same or similar spatial position and/or size similar to b jk , may be assigned to the same given family. 
     In some implementations, a length of a family length(T i ) may comprise a frame spacing between a frame where a text string was first detected and a frame where the text string is last detected. It may be desired to remove incorrectly determined families. By way of non-limiting example, a family having length(T i )&lt;t 2  and/or 
                 #   ⁢     T   i         length   ⁡     (     T   i     )         &lt;     t   3           
may be considered false positives, and/or may be discarded. The “#” term may refer to the number of strings in a given family. Once false positives have been discarded, families that may be close in time (frames) and/or spatial position may be merged.
 
     In some implementations, to ensure the temporal continuity of one or more text strings within a family, one or more proxy text strings may be generated in frames wherein a text string for a given family may not have been detected. Generating proxies may comprise determining if one or more text strings of a given family is static or moving. In some implementations, if the text is static, one or more proxy text strings may be generated that comprise one or more previous text strings of the family. In some implementations, if the text is moving, one or more proxy text strings may comprise an interpolation between a previous existing text string and a subsequent existing text string in the given family. 
     In some implementations, the feature component  110  may be configured such that information (e.g., features, roles of faces, face and/or text family determinations, and/or other information) determined for a given shot may be used for other shots that may be determined to belong to a common group. By way of non-limiting example, determinations made by the feature component  110  and/or other components for a given shot may be saved and used to perform batch operations for other shots in a common group. Batch operations may server to reduce computational load, time, and/or provide other advantages. 
     By way of non-limiting illustration, in  FIG. 2 , the feature component  110  may be configured to determine a first feature  216  and/or other features  218  of a first frame  214  of the first shot  208 , a second feature  222  and/or other features  224  of a second frame  220  of the second shot  210 , and/or other features of other shots  212  of the first video  202 . 
     By way of non-limiting example in  FIG. 3 , exemplary implementations of feature detections for a given frame  300  of a video are depicted. The frame  300  may include an image depicting one or more actors, objects, overlaid text, and/or other features. In some implementations, feature detections may be represented by bounding boxes placed over a corresponding feature. By way of non-limiting example, the feature detections may be associated with a first bounding box  302  that corresponds to a first face detection in frame  300 , a second bounding box  304  that corresponds to a second face detection in the frame  300 , a set of bounding boxes  306  that correspond to one or more other face detections in the frame  300 , a third bounding box  308  that corresponds to a first text detection in the frame  300 , and/or other feature detections. The first bounding box  302 , second bounding box  304 , third bounding box  308 , and/or other bounding boxes depicted by a solid line may represent corresponding features (e.g., faces, text, and/or other features) that may be determined as important (e.g., a lead speaker, news anchor, a headline, and/or other feature determined as important). The set of bounding boxes  306  and/or other bounding boxes depicted by a dashed line may represent corresponding faces that may be determined as unimportant (e.g., background and/or transient actors in the frame). 
     Returning to  FIG. 1 , the group component  112  may be configured to determine one or more groups of shots for individual videos. Individual groups of shots may be determined based on features of the shots and/or other information associated with the shots. By way of non-limiting example, a given group may include one or more shots of a given video that may share similar features. This may include, for example, shots portraying the same persons, actions, environments (e.g., based on similarly detected objects in a shots, such as a building and/or other objects), and/or other similar features. 
     By way of non-limiting example, in a video of an interview, the video may comprise shots that alternate between a set of shots focusing on the interviewer and another set of shots focusing on the interviewee. The group component  112  may be configured to group together shots that focus on the interviewer into one given group, while grouping together shots that focus on the interviewee into another given group. It is noted that the above description of an interview video is provided for illustrative purposes only and is not to be considered limiting. For example, this should not be considered limiting with respect to the shots of a video; the objects, actors, and/or other content portrayed in the video; and/or how shots may be grouped based on similarity. 
     In some implementations, the group component  112  may be configured such that determining one or more groups of shots may include determining a similarity of features between pairs of shots, assigning similar pairs of shots to a common group, and/or assigning dissimilar pairs to separate groups. Pairwise similarity may be determined until one or more of the shots of a video may be either assigned to a group including one or more other shots and/or assigned to its own group (e.g., due to being dissimilar from the other shots of the video). 
     In some implementations, similarity between shots may be determined based on comparing determined features of sample frames selected from individual shots and/or other techniques for determining shot similarity. By way of non-limiting example, the group component  112  may be configured such that determining similarity between pairs comprises one or more of: selecting one or more sample frames from individual shots; comparing features of selected one or more sample frames between a given pair of shots; determining, based on the comparison, whether the given pair of shots are similar; and/or other operations. In some implementations, the group component  112  may be configured such that based on a comparison of features of respective one or more sample frames of the given pair of shots conveying similarity, the given pair of shots may be assigned to a given group. In some implementations, the group component  112  may be further configured such that, based on a comparison of features of respective one or more sample frames of the given pair of shots conveying dissimilarity, individual shots in the given pair may be assigned to separate groups. 
     In some implementations, the group component  112  may be configured to select one or more sample frames from individual shots. The sample frames may be selected based on a sampling frequency, randomly, following predetermined distributions, and/or based on other sampling parameters. By way of non-limiting example, sample frames may be selected from every 10 th  frame, 20 th  frame, and/or other sampling frequency. By way of non-limiting example, a predetermined distribution may include selecting one or more frames from a given temporal segment of the shot including one or more of a middle segment, begging segment, ending segment, and/or other segments. 
     The group component  112  may be configured to determine a measure of similarity between two shots based on the similarities of the selected sample frames. By way of non-limiting example, the group component  112  may be configured to compare features of one or more frames of individual shots to one another. The group component  112  may be configured to assign a similarity measure and/or measures based on the comparison. For example, if a frame of a first shot shares the same feature of another shot, then the pair of shots may be assigned a point value and/or other similarity measure. The final similarity measure between a pair of shots may be the total value of points assigned. In some implementations, a threshold point value may be determined such that total point values assigned to a pair of shots that may be above the threshold may be determined as belonging to the same group. On the other hand, total point values assigned to a pair of shots that may be below the threshold may be determined as belonging to different groups. 
     The above description of point value assignment for similarity measures is provided for illustrative purpose only and is not to be considered limiting. For example, this is not to be considered limiting with respect to how features of pairs of shots may be compared and/or how a similarity measure may be determined based on a comparison. By way of non-limiting example, similarity measures may be numerical (e.g., points, amount, score, rank, ratings, place, grades, or any other type of numerical value), descriptive (e.g., “good match,” “great match,” “bad match,” “worst match,” and/or other description), progressive (e.g., high, medium, low, and/or other description), pictorial (e.g., a thumbs up, a thumbs down, and/or other imagery), and/or any other type of expression representing a measure of similarity between shots. In some implementations, a similarity measure may be normalized such that a value of “1” may describe two shots that may belong to the same group. 
     In some implementations, the group component  112  may be configured such that determining groups of shots may be based on compiling an undirected graph and/or by other techniques. By way of non-limiting example, an undirected graph may be complied such that a given vertex may represent a given shot and/or a given edge may be weighted based on a determined similarity measure. The weights may be viewed as the probability of going from one vertex to another. The group component  112  may be configured to group one or more vertices in the graph using an unsupervised cluster algorithm for graphs based on simulation of flow. By way of non-limiting example, this may provide a technique to assign the vertices into groups. This may include one or more of a Markov Cluster Algorithm, affinity propagation, DBSCAN, and/or other clustering technique used to determine groups. By varying one or more parameters of the algorithm and/or by using a cutoff threshold, the group component  112  may be configured to control a granularity of the clustering for determining shot groups. 
     By way of non-limiting illustration in  FIG. 2 , the group component  112  may be configured such that the first shot  208  may be assigned to a first group  226 , the second shot  210  may be assigned to a second group  228 , and/or other shots may be assigned to one or more other groups  231 . In some implementations, the assignment of the first shot  208  to the first group  226  may be based on the group component  112  selecting the first frame  214  as a sample frame of the first shot  208 . The assignment of the first shot  208  to the first group  226  may be further based on the first feature  216  of the first frame  214  and/or other features of the first shot  208  being similar to features of other shots assigned to the first group  226 . In some implementations, the assignment of the second shot  210  to the second group  228  may be based on the group component  112  selecting the second frame  220  as a sample frame of the second shot  210 . The group component  112  may be configured such that the assignment of the second shot  210  to the second group  228  may be further based on the second feature  222  of the second frame  220  and/or other features of the second shot  210  being different from the first feature  216  and/or other features of shots assigned to the first group  226 . The group component  112  may be configured to assign other shots of the first video  202  to the first group  226 , second group  228 , and/or one or more other groups  231 . 
     Returning to  FIG. 1 , the key frame component  114  may be configured to select individual key frames for individual shots. A given key frame may comprise a frame of a given shot that best represents one or more features determined for one or more frames of the given shot. By way of non-limiting example, a key frame may comprise a frame of a shot that includes one or more of a target image quality, a target face detection, a target text detection, and/or other target features. For example, a “target” feature may correspond to the given feature meeting and/or exceeding a predetermined quality of the given feature. In some implementations, a target feature may be a feature that “best” represents the features of a given group. By way of non-limiting example, a frame may be selected as a key frame based on one or more of the image quality, face detection(s), text detection(s), and/or other features of the frame meeting and/or exceeding predetermined target values for one or more of these features. Key frames may be selected in other ways. 
     In some implementations, a target image quality frame may comprise a frame with no or few artifacts, blurriness, and/or others aspects that may negatively affect an image quality. In some implementations, a target face detection may be associated with a frame that depicts one or more important faces as determined by the feature component  110 . A target face detection may further be associated with a frame that depicts an important face having a target size, target position, and/or target angle of the face; a target state of a mouth and/or eyes; and/or other target features. For example, a target size of a face may a given percentage of an area of a given frame image. A given percentage may be in the range of 10 to 80% of an area of a two-dimensional plane of a given frame image and/or other range. By way of non-limiting example, a target size may be a face that appears proportionally larger than other faces due to the positioning of an actor to a camera capturing an image. A target position may be associated with a given face being positioned in a central location on the frame, a given distance from one or more edges of the frame, a given distance between other faces, and/or other target position. A target angle of the face may be associated with the face being shown in a profile view with respect to camera, plan view with respect to the camera, and/or angle of the face. A target state of a mouth may be closed, smiling, frowning, and/or other states. A target state of the eyes may be open, winking, closed, a direction of gaze, and/or other state. 
     In some implementations, the key frame component  114  may be configured such that determining key frames comprises determining frame scores for individual frames. Frame scores may be determined based on one or more features of a given frame, and/or other information. By way of non-limiting example, frame scores may be determined based on feature scores determined by the feature component  110 , information associated with one or more text string detections, and/or other information. By way of non-limiting example, frame scores may be determined based on one or more of a quality or flattery score, a position score, a distance score, a size score, and/or other scores determined by the feature component  110  and/or other information. 
     In some implementations, a given frame score may convey a quality of the features of a given frame of a given shot relative the features of one or more other frames of the given shot. The frame scores may be utilized to determine if a given frame may best represent a corresponding shot as a key frame of the shot. 
     By way of non-limiting example, the following technique and/or other techniques for selecting key frames may be utilized. In some implementations, for each frame j, 1≦j≦n (“n” denoting the last frame of a shot) in the shot S i  a text score S t,j , a faces score S f,j , a quality score E f,j , and/or other scores may be calculated. A text score may be calculated as: 
     
       
         
           
             
               
                 S 
                 
                   t 
                   , 
                   j 
                 
               
               = 
               
                 
                   t 
                   j 
                 
                 
                   
                     max 
                     
                       1 
                       ≤ 
                       k 
                       ≤ 
                       
                         m 
                         i 
                       
                     
                   
                   ⁢ 
                   
                     t 
                     k 
                   
                 
               
             
             ; 
           
         
       
         
         
           
             where:
 
 t   j =Σ bεB     i   Area( b )* l ( b )* g ( b );
 
           
         
       
    
     l(b) and g(b) may be factors that depend on the start frame s b  and the end e b  of the family the text string b belongs to; 
     
       
         
           
             
               
                 l 
                 ⁡ 
                 
                   ( 
                   b 
                   ) 
                 
               
               = 
               
                 exp 
                 ⁡ 
                 
                   ( 
                   
                     
                       
                         
                           j 
                           - 
                           
                             s 
                             b 
                           
                         
                         
                           
                             ⅇ 
                             b 
                           
                           - 
                           
                             s 
                             b 
                           
                           + 
                           1 
                         
                       
                       - 
                       0.5 
                     
                     
                       2.0 
                       * 
                       
                         0.5 
                         2 
                       
                     
                   
                   ) 
                 
               
             
             ; 
             and 
           
         
       
       
         
           
             
               g 
               ⁡ 
               
                 ( 
                 b 
                 ) 
               
             
             = 
             
               { 
               
                 
                   
                     
                       
                         
                           
                             ⅇ 
                             b 
                           
                           - 
                           
                             s 
                             b 
                           
                           + 
                           1 
                         
                         
                           t 
                           4 
                         
                       
                     
                     
                       
                         
                           
                             ⅇ 
                             b 
                           
                           - 
                           
                             s 
                             b 
                           
                           + 
                           1 
                         
                         ≤ 
                         
                           t 
                           1 
                         
                       
                     
                   
                   
                     
                       0.5 
                     
                     
                       
                         
                           
                             ⅇ 
                             b 
                           
                           - 
                           
                             s 
                             b 
                           
                           + 
                           1 
                         
                         &gt; 
                         
                           
                             t 
                             2 
                           
                           * 
                           
                             length 
                             ⁡ 
                             
                               ( 
                               Shot 
                               ) 
                             
                           
                         
                       
                     
                   
                   
                     
                       1.0 
                     
                     
                       otherwise 
                     
                   
                 
                 . 
               
             
           
         
       
     
     The factor g may be utilized to penalize short and/or long families of text strings. The short families may be a false detection, and the families that exist during almost a majority of the shot may be less important for the selection of the key frame. The factor l may decrease the importance of a text string when it may be near a beginning or an ending frame corresponding to a family of the text string. 
     In some implementations, a face score may be calculated based on whether one or more important families of faces detected in a given frame have been found. If there are important families of faces belonging to a given frame, being I j , a set of faces in frame, f j , that belong to an important face family may be calculated by: 
     
       
         
           
             
               
                 S 
                 
                   f 
                   , 
                   j 
                 
               
               = 
               
                 
                   
                     ( 
                     
                       
                         
                           r 
                           j 
                         
                         2 
                       
                       + 
                       
                         1 
                         4 
                       
                     
                     ) 
                   
                   ⁢ 
                   
                     
                        
                       
                         I 
                         j 
                       
                        
                     
                     
                       
                         max 
                         
                           1 
                           ≤ 
                           k 
                           ≤ 
                           
                             m 
                             i 
                           
                         
                       
                       ⁢ 
                       
                          
                         
                           I 
                           k 
                         
                          
                       
                     
                   
                 
                 + 
                 
                   
                     ( 
                     
                       
                         3 
                         4 
                       
                       - 
                       
                         
                           r 
                           i 
                         
                         2 
                       
                     
                     ) 
                   
                   ⁢ 
                   
                     
                       
                         ∑ 
                         
                           f 
                           ∈ 
                           
                             I 
                             j 
                           
                         
                       
                       ⁢ 
                       
                         Q 
                         ⁡ 
                         
                           ( 
                           f 
                           ) 
                         
                       
                     
                     
                       
                         max 
                         
                           1 
                           ≤ 
                           k 
                           ≤ 
                           
                             m 
                             i 
                           
                         
                       
                       ⁢ 
                       
                         
                           ∑ 
                           
                             f 
                             ∈ 
                             
                               I 
                               k 
                             
                           
                         
                         ⁢ 
                         
                           Q 
                           ⁡ 
                           
                             ( 
                             f 
                             ) 
                           
                         
                       
                     
                   
                 
               
             
             ; 
           
         
       
         
         
           
             where 
           
         
       
    
               r   =         max     1   ≤   k   ≤     m   i         ⁢     (          I   j          )            F            ,         
and Q(f) may comprise the flattery score utilized by the feature component  110 . The score, S f,j , may prioritize getting one or more the important faces of a shot in a selected key frame. In some implementations, the score, S f,j  may give more importance to the flattering faces.
 
     In some implementations, if no face family has been detected as important, a face score may be calculated as: 
     
       
         
           
             
               S 
               
                 f 
                 , 
                 j 
               
             
             = 
             
               
                 
                   
                     
                       ∑ 
                       k 
                     
                     ⁢ 
                     
                       score 
                       ⁡ 
                       
                         ( 
                         
                           Face 
                           jk 
                         
                         ) 
                       
                     
                   
                   
                     
                       max 
                       j 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         k 
                       
                       ⁢ 
                       
                         score 
                         ⁡ 
                         
                           ( 
                           
                             Face 
                             jk 
                           
                           ) 
                         
                       
                     
                   
                 
                 * 
                 0.3 
               
               + 
               
                 
                   
                      
                     
                       f 
                       j 
                     
                      
                   
                   
                     
                       max 
                       j 
                     
                     ⁢ 
                     
                        
                       
                         f 
                         j 
                       
                        
                     
                   
                 
                 * 
                 
                   0.7 
                   . 
                 
               
             
           
         
       
     
     In some implementations, a quality score may be calculated by applying a filter to a grayscale version of the frame, f i ′. The filter may be a Iaplacian filter such as: 
     
       
         
           
             
               
                 l 
                 i 
               
               = 
               
                 
                   f 
                   i 
                   ′ 
                 
                 * 
                 
                   ( 
                   
                     
                       
                         0 
                       
                       
                         1 
                       
                       
                         0 
                       
                     
                     
                       
                         1 
                       
                       
                         
                           - 
                           4 
                         
                       
                       
                         1 
                       
                     
                     
                       
                         0 
                       
                       
                         1 
                       
                       
                         0 
                       
                     
                   
                   ) 
                 
               
             
             ; 
           
         
       
         
         
           
             where “*” may denote a convolution operator. 
           
         
       
    
     In some implementations, for individual frames of a given shot, an energy score may be calculated as the mean of the squared values of the resulting pixels: 
     
       
         
           
             
               ⅇ 
               i 
             
             = 
             
               
                 1 
                 
                   # 
                   ⁢ 
                   
                     l 
                     i 
                   
                 
               
               ⁢ 
               
                 
                   ∑ 
                   
                     p 
                     ∈ 
                     
                       l 
                       i 
                     
                   
                 
                 ⁢ 
                 
                   
                     p 
                     2 
                   
                   . 
                 
               
             
           
         
       
     
     In some implementations, the quality score of a frame may then be calculated as: 
     
       
         
           
             
               S 
               
                 e 
                 , 
                 i 
               
             
             = 
             
               
                 ⅇ 
                 i 
               
               
                 
                   max 
                   
                     1 
                     ≤ 
                     j 
                     ≤ 
                     i 
                   
                 
                 ⁢ 
                 
                   ( 
                   
                     ⅇ 
                     j 
                   
                   ) 
                 
               
             
           
         
       
     
     In some implementations, the final frame score, S i , for a given frame may be calculated as:
 
 S   i   =w   f   *S   f,i   +w   t   *S   t,i   +w   e   *S   e,i ;
         where w f , w t , w e  may be weights that change the relative importance of the different scores. The key frame component  114  may be configured to select an individual frame for an individual shot based on the final frame scores determined for individual ones of the frames of that shot, and/or other information. By way of non-limiting example, the frame with the highest final frame score may be selected as a key frame.       

     By way of non-limiting illustration in  FIG. 2 , the key frame component  114  may be configured such that the first frame  214  may be selected as a first key frame  230  of the first shot  208 , the second frame  220  may be selected as a second key frame  232  of the second shot  210 , and/or other frames of other shots of the first video may be selected as other key frames  234  of the other shots  212  of the first video  202 . In some implementations, the key frame component  114  may be configured to determine a first frame score (not shown in  FIG. 2 ) for the first frame  214 , and/or other frame scores for other frames of the first video  202 . The key frame component  114  may be configured such that the first frame  214  may be selected as the first key frame  230  of the first shot  208  based on the first score conveying that the first frame  214  provides a best representation of the first feature  216  and/or other features of the first shot  208 . 
     Returning to  FIG. 1 , the storyboard component  116  may be configured to effectuate presentation of the key frames in storyboard interfaces corresponding to individual ones of the videos. In some implementations, a given storyboard interface may display the key frames of a given video in a chronological order of the corresponding shots. Key frames of grouped shots may be spatially correlated within a given storyboard interface. 
     By way of non-limiting example, an individual storyboard interface may include a two dimensional array. The two dimensional array may include a time-dependent dimension, a group-dependent dimension, and/or other dimensions. A chronological order of the shots of a given video may be represented by positions of key frames of the video with respect to the time-dependent dimension of a corresponding storyboard interface. A grouping of the shots of a given video may be visually represented by spatial positions of the key frames of the given video with respect to the group-dependent dimension. By way of non-limiting example, groupings of shots may be represented by in-line positioning of corresponding key frames of the shots in the storyboard interface. In some implementations, a storyboard interface may include a time-dependent dimension and an orthogonally opposed group-dependent dimension (see, e.g.,  FIG. 4 ). 
     By way of non-limiting illustration in  FIG. 4 , an exemplary storyboard interface  400  is depicted. The storyboard interface  400  may include a time-dependent dimension  402 , a group-dependent dimension  404 , and/or other components. A chronological order of the shots of a given video may be represented by positions of key frames  408  of the video with respect to the time-dependent dimension  402 . For example, time-dependent dimension  402  may include a “start” end and/or an “end” end to represent the chronological order of the shots corresponding to the depicted key frames  408 . A grouping of the shots of the given video may be visually represented by spatial positions of the key frames  408  with respect to the group-dependent dimension  404 . By way of non-limiting example, a given group of shots may be represented by in-line positioning of the key frames of the shots of the given group in the storyboard interface. By way of non-limiting example, grouped shots may be represented by in-line positioning of corresponding key frames  408  along individual rows  406   a - i  of the storyboard interface  400 . Individual ones of the rows  406   a - i  may represent individual groups of shots. 
     One or more implementations of a storyboard interface may provide an intuitive view of a corresponding video. For example, patterns in groupings of key frames may be utilized to distinguish one type of video from another; determine what shots and/or group of shots include particular actors (e.g., interviewee versus interviewer, and/or other actors), scenes, and/or other features; and/or to determine other information about a given video. By way of non-limiting example, types of video may include an interview and/or news program, an action sports montage, a music video, and/or other types of video. 
     By way of non-limiting example, an interview and/or news program may be distinguishable from other video types based on patterns of key frames presented in a corresponding storyboard interface. In such videos, the key frames presented in a storyboard interface may alternate between different groups of key frames and a common group of key frames. For example, one or more groups of key frames may represent shots where various persons may be interviewed. A common group of key frames may represent a news anchor in a news room. A pattern of key frames alternating from one or more groups to a common group may represent a consistent pattern in the interview process where the camera cuts from shots of those being interviewed to a central shot the news room. When a user ascertains that a common group of shots is present, the user may be able to further ascertain the individuals who may be the interviewers (e.g., news anchors) in the video. 
     By way of non-limiting example, a music video may be distinguishable from other video types based on patterns of key frames presented in a corresponding storyboard interface. In such videos, the key frames presented in a storyboard interface may alternate between wide angle frames depicting a group of actors, close-up shots depicting a head shot of a single actor, and/or other key frames. For example, the wide angle frames may represent views of a band. Close-up shots may represent views of individual band members. A user viewing the storyboard interface may further be able to determine which shots show the band leader due to, for example, a group of close-up key frames including the most amount of key frames relative other groups of key frames. 
     By way of non-limiting illustration in  FIG. 2 , the storyboard component  116  may be configured to effectuate presentation of the first key frame  230 , the second key frame  232 , and/or other key frames of the first video  202  in a first storyboard interface  236 . The first key frame  230  and/or other key frames of other shots assigned to the first group  226  may be spatially correlated within the display of the first storyboard interface  236 . By way of non-limiting example, the first key frame  230  may be posited in-line with key frames of others shots assigned to the first group  226 . In some implementations, the first storyboard interface  236  may comprise a two-dimensional array. The first storyboard interface  236  may include a time-dependent dimension  238 , a group-dependent dimension  240 , and/or other dimensions. The storyboard component  116  may be configured to effectuate presentation of other storyboard interfaces  242  corresponding to other videos  206 . 
     Returning to  FIG. 1 , the server  102 , computing platforms  122 , and/or external resources  124  may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network  121  such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting and that the scope of this disclosure includes implementations in which server  102 , computing platforms  122 , and/or external resources  124  may be operatively linked via some other communication media. 
     The external resources  124  may include sources of information, hosts, and/or providers of virtual spaces outside of system  100 , external entities participating with system  100 , external entities for player-to-player communications, and/or other resources. In some implementations, some or all of the functionality attributed herein to external resources  124  may be provided by resources included in system  100 . 
     The server  102  may include electronic storage  118 , one or more processors  104 , and/or other components. The server  102  may include communication lines or ports to enable the exchange of information with a network and/or other computing platforms. Illustration of server  102  in  FIG. 1  is not intended to be limiting. The server  102  may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to server  102 . For example, server  102  may be implemented by a cloud of computing platforms operating together as server  102 . 
     Electronic storage  118  may comprise electronic storage media that electronically stores information. The electronic storage media of electronic storage  118  may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with server  102  and/or removable storage that is removably connectable to server  102  via, for example, a port or a drive. A port may include a USB port, a firewire port, and/or other port. A drive may include a disk drive and/or other drive. Electronic storage  118  may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storage  118  may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). Electronic storage  118  may store software algorithms, information determined by processor  104 , information received from server  102 , information received from computing platforms  122 , and/or other information that enables server  102  to function as described herein. 
     Processor(s)  104  is configured to provide information-processing capabilities in server  102 . As such, processor  104  may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although processor  104  is shown in  FIG. 1  as a single entity, this is for illustrative purposes only. In some implementations, processor  104  may include one or more processing units. These processing units may be physically located within the same device, or processor  104  may represent processing functionality of a plurality of devices operating in coordination. The processor  104  may be configured to execute components  108 ,  110 ,  112 ,  114 , and/or  116 . Processor  104  may be configured to execute components  108 ,  110 ,  112 ,  114 , and/or  116  by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on processor  104 . 
     It should be appreciated that although components  108 ,  110 ,  112 ,  114 , and/or  116  are illustrated in  FIG. 1  as being co-located within a single processing unit, in implementations in which processor  104  includes multiple processing units, one or more of components  108 ,  110 ,  112 ,  114 , and/or  116  may be located remotely from the other components. The description of the functionality provided by the different components  108 ,  110 ,  112 ,  114 , and/or  116  described above is for illustrative purposes and is not intended to be limiting, as any of components  108 ,  110 ,  112 ,  114 , and/or  116  may provide more or less functionality than is described. For example, one or more of components  108 ,  110 ,  112 ,  114 , and/or  116  may be eliminated, and some or all of its functionality may be provided by other ones of components  108 ,  110 ,  112 ,  114 ,  116 , and/or other components. As another example, processor  104  may be configured to execute one or more additional components that may perform some or all of the functionality attributed below to one of components  108 ,  110 ,  112 ,  114 , and/or  116 . 
       FIG. 5  illustrates a method  500  of automatic key frame extraction and storyboard interface generation for videos. The operations of method  500  presented below are intended to be illustrative. In some implementations, method  500  may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method  500  are illustrated in  FIG. 5  and described below is not intended to be limiting. 
     In some implementations, method  500  may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method  500  in response to instructions stored electronically on electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method  500 . 
     At an operation  502 , one or more videos having multiple frames may be obtained. The obtained videos may include a first video having a first set of frames. In some implementations, operation  502  may be performed by one or more physical processor executing a shot component that is the same as or similar to shot component  108  (shown in  FIG. 1  and described herein). 
     At an operation  504 , discrete shots in the individual videos may be identified. A given shot in a given video may include consecutive frames of the given video. By way of non-limiting example, a first shot and a second shot of the first video may be identified. In some implementations, operation  504  may be performed by one or more physical processors executing a shot component that is the same as or similar to the shot component  108  (shown in  FIG. 1  and described herein). 
     At an operation  506 , features of individual frames may be determined. In some implementations, operation  506  may be performed by one or more physical processors executing a feature component that is the same as or similar to the feature component  110  (shown in  FIG. 1  and described herein). 
     At an operation  508 , one or more groups of shots for individual videos may be determined based on the determined features of individual frames. A given group may include one or more shots of a given video having the same or similar features. By way of non-limiting example, responsive to determining that a first frame of the first shot includes a first feature and a second frame of the second shot includes a second feature, determining that the first shot is assigned to a first group based on the first feature being similar to other features of other shots assigned to the first group, and determining that the second shot is assigned to a second group based on the second feature being different from the first feature. In some implementations, operation  508  may be performed by one or more physical processors executing a group component that is the same as or similar to the group component  112  (shown in  FIG. 1  and described herein). 
     At an operation  510 , individual key frames representative of the individual shots may be identified. By way of non-limiting example, the first frame may be selected as a first key frame of the first shot, the second frame may be selected as a second key frame of the second shot, and/or other frames may be selected as other key frames of other shots. In some implementations, operation  510  may be performed by one or more physical processors executing a key frame component that is the same as or similar to the key frame component  114  (shown in  FIG. 1  and described herein). 
     At an operation  512 , presentation of the key frames in storyboard interfaces may be effectuated. A given storyboard interface of given video may display corresponding key frames in a chronological order of the corresponding shots of the given video. Key frames of grouped shots of the given video may be spatially correlated within the given storyboard interface. By way of non-limiting example, the first key frame and the second key frame may be presented in a first storyboard interface. The first key frame and/or other key frames of other shots assigned to the first group may be spatially correlated in the first storyboard interface. In some implementations, operation  512  may be performed by one or more physical processors executing a storyboard component that is the same as or similar to the storyboard component  116  (shown in  FIG. 1  and described herein). 
     Although the present technology has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the technology is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present technology contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.
A storyboard interface displaying key frames of a video may be presented to a user. Individual key frames may represent individual shots of the video. Shots may be grouped based on similarity. Key frames may be displayed in a chronological order of the corresponding shots. Key frames of grouped shots may be spatially correlated within the storyboard interface. For example, shots of a common group may be spatially correlated so that they may be easily discernable as a group even though the shots may not be temporally consecutive and/or or even temporally close to each other in the timeframe of the video itself.