A method and apparatus are provided for organizing media files using parameters obtained from the visual content and metadata of the media files. Using machine learning, an algorithm is trained to apply user preferences to organize the media files. The user indicates their preferences by viewing the media files and selecting relevancy measures and organizational actions for a subset of the media files (i.e., training data). Using the media-file parameters, the algorithm calculates relevancy values for respective media files. The algorithm is trained to minimize the error between the calculated relevancy value and the user determined relevancy measures of the training data. The media-file parameters can include, e.g., the blurriness of and facial and pattern recognition of the visual content; the source, location, time, edit history, and the frequency and recency of access to the media files as recoded in the metadata.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of organizing a plurality of media files including respective digital images, the method comprising: obtaining training data, the training data including a first subset of the plurality of media files and corresponding relevance measures, the relevance measures having been determined by a user; training, via processing circuitry, a relevancy algorithm by adjusting weight values of a weighted sum of the relevancy algorithm to decrease a cost function representing a difference between the relevance measures of the first subset and corresponding relevancy values calculated using the relevancy algorithm, the weighted sum of the relevancy algorithm being a summation over metadata parameters and digital-image parameters of a media file of the first subset; and performing, via the processing circuitry, predefined actions on the plurality of media files according to the relevancy values of the plurality of media files calculated using the relevancy algorithm. 2. The method of organizing the plurality of media files according to claim 1, further comprising: selecting the first subset to represent a diversity among the metadata parameters and the digital-image parameters of the plurality of media files; and obtaining the training data by, for each media file of the first subset, displaying a representation of a digital image corresponding to a media file of the first subset, receiving an input indicating a relevancy measure of the media file to generate the relevancy measure of the media files, associating, via the processing circuitry, the relevancy measure with the media file. 3. The method of organizing the plurality of media files according to claim 1, further comprising: calculating, using the relevancy algorithm, relevancy values corresponding to the plurality of media files; and sorting, via the processing circuitry, the plurality of media files into action classes each corresponding to one of the predefined actions. 4. The method of organizing the plurality of media files according to claim 1, further comprising: calculating, via the processing circuitry and using the relevancy algorithm, confidence values of the respective media files of the plurality of media files, each confidence value representing an uncertainty of either a relevancy value of a media file of the plurality of media files or a predefined action to be performed on the media file. 5. The method of organizing the plurality of media files according to claim 4, further comprising; generating supplemental training data by selecting, using the confidence values, a second subset of the plurality of media files; obtaining relevancy measures of media files of the supplemental training data, the relevance measures of the supplemental training data having been determined by the user; and further adjusting the weight values of the relevancy algorithm to decrease a cost function including the training data and the supplemental training data, the cost function representing a difference between the relevance measures and corresponding relevancy values determined using the relevancy algorithm, wherein the second subset of the plurality of media tiles includes media files corresponding to a large uncertainty corresponding to either an assignment of the relevancy value or an assignment of the predefined action. 6. The method of organizing the plurality of media files according to claim 1, wherein the predefined actions performed on the plurality of media files include storing a media file into at least one folder configured for frequent usage, when the relevancy value corresponding to the media files satisfies a first predefined criteria; compressing the media file and storing the compressed media file into at least one folder configured for archival usage, when the relevancy value corresponding to the media files satisfies a second predefined criteria, and discarding the media file into a trash folder, when the relevancy value corresponding to the media files satisfies a third predefined criteria, wherein the first predefined criteria, the second predefined criteria, and the third predefined criteria are mutually exclusive. 7. The method of organizing the plurality of media files according to claim 1, wherein the relevancy algorithm calculates the relevancy values using the metadata parameters and the digital-image parameters of respective media files, wherein the metadata parameters and the digital-image parameters include one or more of a facial-recognition parameter, a source parameter indicating a source of the media file, the source of the media file being a user of a device used to generate the media file, a website originating the media or a designation of an origin of the media file, a location parameter indicating a location at which the media file was generated, a time parameter indicating a time at which the media file was generated, p1 an edit-history parameter indicating a history of editing, annotating, cropping, or filtering of the media file, a sharing parameter indicating a sharing of the media file on social media or a sharing of the media file with other users, a copying parameter providing indicia of the media file being copied, a frequency-of-access parameter indicating a frequency with which the media file has been accessed, a recency-of-access parameter indicating how recently the media file has been accessed, a blurriness parameter indicating a sharpness or a focus of the media file, a pattern recognition parameter indicating spatial patterns in the media file, and a manual settings parameter indicating manual settings of the device used to obtain the media file. 8. The method of organizing the plurality of media files according to claim 1, wherein the relevancy algorithm includes one or more of a weighted sum over predefined parameters, an artificial neural network, and a scale-invariant feature transform algorithm. 9. The method of organizing the plurality of media files according to claim 1, wherein the adjusting of the weight values of the relevance measure is performed using an optimization method that includes one or more of a back-propagation method, a Nelder-Mead simplex method, a gradient-descent method, a Newton's method, a conjugate gradient method, a shooting method, an expectation-maximization method, a non-parametric method, a particle swarm optimization method, a genetic algorithm method, a simulated annealing method, an interval method, a stochastic method, a heuristic method, and a metatheuristic method. 10. The method of organizing the plurality of media files according to claim 1, wherein the relevancy algorithm is trained using machine learning operating on the training data to assign a relevancy value to a media file of the plurality of media files, the relevancy value being assigned to approximate the relevancy measures of a subset of the training data that has the metadata parameters and the digital-image parameters that are similar to the metadata parameters and the digital-image parameters of the media file. 11. The method of organizing the plurality of media files according to claim 1, wherein the cost function includes one or more error measures indicating a difference between the calculated relevancy values and the corresponding user-determined relevancy measures of the training data, the one or more error measures including one or more of an L1-norm, an L2-norm, and a maximum-likelihood measure. 12. The method of organizing the plurality of media files according to claim 1, wherein the adjusting of the weight values of the weighted sum of the relevancy algorithm further includes calculating the relevancy values using a convolution neural network including a convolution layer and a pooling layer to determine image patterns of a digital image of the media file, and determining, using the determined image patterns, one or more of a facial recognition parameter and a pattern-recognition parameter used to calculate the relevancy values. 13. An apparatus, comprising: a display configured to display a digital image representing a media file of a plurality of media files; an interface configured to receive input from a user in response to the digital image displayed on the display; and processing circuitry configured to determine a first subset of the plurality of media files, control the display to display the digital image representing the media file, determine a relevancy measure associated with the media file based on the input of the user in response to the digital image displayed on the display, generate training data, the training data including the first subset of the plurality of media files and the associated relevance measures of the first subset, calculate a relevancy value of a media file of the plurality of media files using a relevancy algorithm that includes a weighted sum over metadata parameters and digital-image parameters of the media file, train the relevancy algorithm by adjusting weight values of the relevancy algorithm to decrease a cost function representing a difference between the relevance measures of the training data and the corresponding relevancy values calculated using the relevancy algorithm, and perform predefined actions on the plurality of media files according to the relevancy values of the plurality of media files calculated using the relevancy algorithm. 14. The apparatus according to claim 13, wherein the processing circuitry is further configured to calculate, using the relevancy algorithm, confidence values of the plurality of media files, each confidence value representing an uncertainty of either a relevancy value of a media file of the plurality of media files or a predefined action to be performed on the media file. 15. The apparatus according to claim 14, wherein the processing circuitry is further configured to generate supplemental training data by selecting, using the confidence values, a second subset of the plurality of media files, obtain relevancy measures of media files of the supplemental training data, the relevance measures of the supplemental training data having been determined by the user, and further adjust the weight values of the relevancy algorithm to decrease a cost function including the training data and the supplemental training data, the cost function representing a difference between the relevance measures and corresponding relevancy values determined using the relevancy algorithm, wherein the second subset of the plurality of media files includes media files corresponding to a large uncertainty corresponding to either an assignment of the relevancy value or an assignment of the predefined action. 16. The apparatus according to claim 13, wherein the processing circuitry is further configured to perform the predefined actions on the plurality of media files by storing a media file into at least one folder configured for frequent usage, when the relevancy value corresponding to the media files satisfies a first predefined criteria; compressing the media file and storing the compressed media file into at least one folder configured for archival usage, when the relevancy value corresponding to the media files satisfies a second predefined criteria, and discarding the media file into a trash folder, when the relevancy value corresponding to the media files satisfies a third predefined criteria, wherein the first predefined criteria, the second predefined criteria, and the third predefined criteria are mutually exclusive. 17. The apparatus according to claim 13, wherein the processing circuitry is further configured to calculate the relevancy values using one or more of a weighted sum over predefined parameters, an artificial neural network, and a scale-invariant feature transform algorithm. 18. The apparatus according to claim 13, wherein the processing circuitry is further configured to calculate the relevancy values using the metadata parameters and the digital-image parameters of respective media files, wherein the metadata parameters and the digital-image parameters include one or more of a facial-recognition parameter, a source parameter indicating a source of the media file, the source of the media file being a user of a device used to generate the media file, a website originating the media file, or a designation of an origin of the media file, a location parameter indicating a location at which the media file was generated, a time parameter indicating a time at which the media file was generated, an edit-history parameter indicating a history of editing, annotating, cropping, or filtering of the media file, a sharing parameter indicating a sharing of the media file on social media or a sharing of the media file with other users, a copying parameter providing indicia of the media file being copied, a frequency-of-access parameter indicating a frequency with which the media file has been accessed, a recency-of-access parameter indicating how recently the media file has been accessed, a blurriness parameter indicating a sharpness or a focus of the media file, a pattern recognition parameter indicating spatial patterns in the media file, and a manual settings parameter indicating manual settings of the device used to obtain the media file. 19. The apparatus according to claim 13, wherein the processing circuitry is further configured to calculate the relevancy values using a convolution neural network including a convolution layer and a pooling layer to determine image patterns of a digital image of the media file, and determine, using the determined image patterns, one or more of a facial/object recognition parameter and a pattern-recognition parameter used to calculate the relevancy values. 20. A non-transitory computer-readable medium storing executable instructions, wherein the instructions, when executed by processing circuitry, cause the processing circuitry to perform a method comprising steps of: obtaining training data, the training data including a first subset of the plurality of media files and corresponding relevance measures, the relevance measures having been determined by a user; training, via processing circuitry, a relevancy algorithm by adjusting weight values of a weighted sum of the relevancy algorithm to decrease a cost function representing a difference between the relevance measures of the first subset and corresponding relevancy values calculated using the relevancy algorithm, the weighted sum of the relevancy algorithm being a summation over metadata parameters and digital-image parameters of a media file of the first subset; and performing, via the processing circuitry, predefined actions on the plurality of media files according to the relevancy values of the plurality of media files calculated using the relevancy algorithm.