In this paper , we propose a fast motion estimation technique using chroma interpolation filter for High Efficiency Video Coding ( HEVC ) encoder . In order to reduce the computation time of motion estimation process , the proposed method utilizes 4-tap chroma interpolation filter for motion estimation and 8-tap luma interpolation filter for motion compensation , separately , instead of applying the same 8-tap luma interpolation filter to both motion estimation and motion compensation process . From the experimental results , the proposed algorithm achieves 4.63 % time-saving with 0.06 % BD-bitrate increase on average , compared to x265 encoder , which is the most computationally efficient HEVC encoder in the industrial field . Additionally , an optimization method based on the artificial bee colony ( ABC ) algorithm is proposed to improve the reconstruction performance . Using the proposed algorithm , the motion compensation problem for the single-RF-channel DBF array can be solved effectively , and the angle and Doppler information for the target can be simultaneously estimated . The effectiveness of the proposed algorithms is demonstrated by the results of numerical simulations . We build on our prior work on best basis selection algorithms in large tree-structured dictionaries , previously used to construct effective image coders based on block and lapped transforms , as well as to improve the motion compensation step in video compression . In the present paper , we use such algorithms to select the optimal transform tiling for video compression . Also , the mesh is coded as a tree and a sequence of motion vectors to ensure low bit rate . In the motion compensation part , using a modified affine transform that reduces the complexity of the motion compensation reduces the computational cost . The performance of this architecture is also presented . A methodology to compare IR estimation with and without motion is presented . Based on this comparison , a method for motion effect compensation is proposed in order to reduce motion-induced distortions . The proposed methodology is applied to real data sets collected in 2007 by the Service Hydrographique et Océanographique de la Marine in a shallow water environment , proving its interest for motion effect analysis . A methodology to compare the IR estimation in motionless and motion contexts is presented . Based on this comparison , a warping‐based lag‐Doppler filtering method in the wideband ambiguity plane for motion effect compensation is proposed . It aims at reducing the distortions due to the motion phenomena and accurately estimating the IR of the channel . The paper assumes a working knowledge of MPEG-2 coding , the standard of which is now readily available . The results used in this paper were obtained using a straightforward set of software based on two programs : a motion vector estimator generating a file of best motion vectors for each macroblock and , an encoder program performing motion vector compensation , DCT coding , quantisation , runlength coding and entropy calculation . In this paper , the implementation of H.263 real-time encoding on DSP-TM13XX is described . This implementation method can be easily extended to other standards such as H.263 + , MPEG-i , MPEG-2 , MPEG-4 etc . , because these standards are essentially very similar ( i.e. Motion Compensate , Discrete Cosine Transform , Quantization and Entropy Coding ) . In addition , TM13XX series of DSPs utilize a very powerful , general purpose VLIW ( Very Long Instruction Word ) processor core which makes them ideal for the high performance embedded multimedia applications . This paper builds an aerocraft ' nonideal moiotn model , quantificationaUy analysed flight path data , estimates different aerocraft 's motion parameter . It is gained that the aerotransport best suits the SAR platform , the airship SAR 's motion compensation is the most difficult , unmanned aircraft 's motion characteristic has lots of differences , which motion compensation requirements should be particularly analysed . When adapting a method from images to videos it is usually beneficial to incorporate the prior knowledge that frames of the same scene of a video can be approximated by a single image and a motion pattern . Estimating and compensating motion is a powerful mechanism to further constrain the problem and expose temporal correlations . It is therefore very common to find video SR methods that explicitly model motion through frames . This nevertheless requires frame preprocessing and is usually expensive . Alternatively , motion compensation can also be performed jointly with the SR task , as done in the Bayesian approach of Liu et al . by iteratively estimating motion as part of its wider modeling of the downscaling process . The advent of neural network techniques that can be trained from data to approximate complex nonlinear functions has set new performance standards in many applications including SR . The spatial transformer and SR modules are both differentiable and therefore end - to - end trainable . As a result , they can be jointly optimised to minimise a composite loss combining the accuracy of the reconstruction in eq : image - sr - objective with the fidelity of motion compensation in eq : motion - compensation - objective , namely section : Experiments and results The models investigated are extended with a motion compensation mechanism based on spatial transformer networks that is efficient and jointly trainable for video SR . Results obtained with approaches that incorporate explicit motion compensation are demonstrated to be superior in terms of PSNR and temporal consistency compared to spatio - temporal models alone , and outperform the current state of the art in video SR . bibliography : References This paper describes a video summarization and semantics editing tool that is suited for content-based video indexing and retrieval with appropriate human operator assistance . The whole system has been designed with a clear focus on the extraction and exploitation of motion information inherent in the dynamic video scene . Indeed , experimental results show that a model-based algorithm outperforms state-of-the-art methods on predicting visual sentiment , visual realism and interestingness . Our paradigm can be used in various visual tasks ( e.g. , video summarization ) . Experimentation done with the help of the test bed of videos has shown that higher codebook sizes of LBG have given better completeness in key frame extraction for video summarization . Experimental results are also discussed to represent the validity of the proposed method for video content summarization . The existing methods can only extract the static images of videos as the content summarization , but they ignore the representation of motion information . To cope with these issues , a novel framework for an efficient video content summarization as well as video motion summarization is proposed . Initially , Capsules Net is trained as a spatiotemporal information extractor , and an inter-frames motion curve is generated based on those spatiotemporal features . Based on the assumption that video features and textual features are highly correlated with each other , we can transfer textual information into knowledge on summarization using video information only . In particular , we formulate the video summarization problem as that of learning a mapping from a set of shots of a video to a subset of the shots using the general framework of SVM-based structured learning . Textual information is transferred by encoding them into a set of constraints used in the structured learning process which tend to provide a more detailed and accurate characterization of the different subsets of shots . Video summarization is a simplification of video content for compacting the video information . The video summarization problem can be transformed to a clustering problem , in which some frames are selected to saliently represent the video content . In this work , we use a hierarchical graph-based clustering method for computing a video summary . Most of the consecutive frames do have very little discriminative contents . In video summarization process , several frames containing similar information need to get processed , this leads to slower processing speed and higher complexity , consuming . Video more time summarization using key frames can ease the speed up of video processing . We argue that video summarization should be an interactive , mixed-initiative process in which users have control over the summarization procedure while algorithms help users achieve their goal via video understanding . In this paper , we introduce ElasticPlay , a mixed-initiative approach that combines an advanced video summarization technique with direct interface manipulation to help users control the video summarization process . Users can specify a time budget for the remaining content while watching a video ; our system then immediately updates the playback plan using our proposed cut-and-forward algorithm , determining which parts to skip or to fast-forward . This method can handle both symmetric and asymmetric group activities . Video based summarization have the potential to assist in maintaining public safety and Efficiently access the record image of surveillance system is an important research goal . This paper proposes a simple method to enhance the video image summarization . The video summarization production process in this paper is divided into intrusion detection of moving objects and time axis with animation output . An Eyetracking Study of How People View Digital Video Surrogates.- A State Transition Analysis of Image Search Patterns on the Web.- Towards a Comprehensive Survey of the Semantic Gap in Visual Image Retrieval.- Applications (Oral).- Audio-Based Event Detection for Sports Video.- Spectral Structuring of Home Videos.- Home Photo Retrieval : Time Matters.- Automatic Annotation of Tennis Action for Content-Based Retrieval by Integrated Audio and Visual Information.- Indexing of Personal Video Captured by a Wearable Imaging System.- Semantics/Learning II (Poster).- Constructive Learning Algorithm-Based RBF Network for Relevance Feedback in Image Retrieval.- Spatial-Temporal Semantic Grouping of Instructional Video Content.- A Novel Scheme for Video Similarity Detection.- Concept-Based Retrieval of Art Documents.- Video Retrieval II (Poster).- Video Retrieval of Human Interactions Using Model-Based Motion Tracking and Multi-layer Finite State Automata.- Fast Video Retrieval under Sparse Training Data.- Robust Content-Based Video Copy Identification in a Large Reference Database.- Video Summarization & Analysis (Poster).- ANSES : Summarisation of News Video.- Spatio-Temporal Decomposition of Sport Events for Video Indexing.- Audio-Assisted Scene Segmentation for Story Browsing.- Performance (Poster).- Performance Comparison of Different Similarity Models for CBIR with Relevance Feedback.- EBS k-d Tree : Saliency prediction is valuable in many video applications , such as intelligent retrieval , advertisement design and delivering , video coding and video summarization generating . Although image saliency is well explored , less works have been done on videos . Specifically , given a starting video frame , we first detect its salient regions based on extracted color and orientation maps ; then we track these regions for the rest of frames within a short period of time . The extracted salient region information could be used to evaluate the frame importance so as to provide guidance in keyframe extraction for video summarization purpose . Experiments on four test video sequences have yielded encouraging results . Furthermore , the average prediction time taken by the proposed methodology is approximately 1.13 seconds which demonstrates the real-time monitoring efficiency of the system . The calculated outcomes show that the proposed methodology performs better contrasted with other contemporary studies for activity prediction , data processing cost , error rate , and time complexity . While GTW treats different portions of a sequence as equally important , our task is in favor of early portions since an incomplete activity video always aligns from the beginning of a complete one . Thus motivated , we develop a temporally-weighted GTW ( TGTW ) algorithm for the activity prediction problem by encouraging alignment in the early portion of an activity sequence . Finally , the similarity derived from TGTW is combined with the k-nearest neighbors algorithm for predicting the activity class of an input sequence . The current tools for proteolytic cleavage prediction have often relied purely on regular expressions , or involve models that do not represent biological data well . RESULTS A novel methodology for characterizing proteolytic cleavage site activities has been developed , which incorporates two fundamental features : activity class prediction and the use of an amino acid similarity matrix for ( non-parametric ) neural learning . The first solved the problem of predicting proteolytic efficiency . The second significantly improved the robustness in prediction and reduced the time complexity for learning . This study shows that activity class prediction is successful when applying this methodology to the prediction and characterization of Trypsin cleavage sites and the prediction of HIV protease cleavage sites . AVAILABILITY Requests for software and data should be made respectively to Dr Zheng Rong Yang and Miss Rebecca Thomson . These approaches consider only information about each individual user and , thus , do not exploit the social connections and structure of the network . In this paper , we approach the problem of activity change prediction at the level of communities rather than individuals . We develop predictive models of activity change over communities obtained using state-of-art community detection methods and compare their predictive power with each other and against the single-user baseline and ego networks . This communication puts forward a new approach for airport investment risk assessment . The approach takes explicitly into account the degree of uncertainty in activity levels prediction and proposes milestones for the different stages of the project for minimizing risk . Uncertainty is represented through fuzzy dual theory and risk management is performed using dynamic programming In this paper , we present the various stages of Technological assistance and propose a new algorithm for the step of activities models detection . We also explore an activity prediction step using time series . The experiments were conducted on real data recorded at LIARA smart home and the results are satisfactory . In this work , we introduce new QSAR models ( quantitative structure-activity relationship ) based on the GTM method ( generative topographic mapping ) , introduced by C. Bishop et al. A part of this thesis is dedicated to the visualization and analysis of large chemical libraries using the incremental version of GTM . We also introduce a new method coined “ Stargate GTM ” or S-GTM , which allows us to travel from the space of chemical descriptors to activity space and vice versa ; this approach was applied to activity profile prediction and inverse QSAR . This paper proposes a model concerned with healthcare analytics at the individual level , which can predict human physical activity status from sequential lifelogging data collected from wearable sensors . The model has a two-stage hybrid structure ( in short , MOGP-HMM ) -- a multi-objective genetic programming ( MOGP ) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov model ( HMM ) in the second stage for activity status prediction over time . It can be used as a decision support tool to provide real-time monitoring , statistical analysis and personalized advice to individuals , encouraging positive attitudes towards healthy lifestyles . Also , we provide an effective solution for the optimization problems . Moreover , we exploit the learned representations of users for the next activity type prediction . Finally , we present extensive experimental results to demonstrate the improved performances of the proposed method . A prototype has been built to prove the feasibility of the proposed architecture and evaluate its performances . The results show that our proposed distributed video conferencing service can actually reduce the average bandwidth required for data and signaling messages at wireless mobile terminals while maintaining the main operations of a video conference session . But since the football video screen changes rapidly , which increases the difficulty of segmentation . This paper first introduces five commonly used methods of shot segmentation , and then introduces three methods for soccer video segmentation , and finally gives a combination of methods used in soccer video segmentation . Hope that this article allows you to more quickly understand the methods of the shot segmentation about soccer videoes . In this paper , we present a novel system and effective algorithms for soccer video segmentation . The output , about whether the ball is in play , reveals high-level structure of the content . Any anti-piracy strategy requires not only copy detection but also precise frame alignment of pirate video with master content , prior to the estimation of geometric distortions and capture location in a theater . Most studies in pirate video registration focus on the alignment of watermarked sequences , while few efforts are made to align non-watermarked videos using content-based features . In this paper , we propose a spatio-temporal scheme for aligning pirate and master contents using visual features , which consists of three stages : First , a video sequence is compactly represented using 1-D SURF ( Speeded-Up Robust Features ) signatures ; Second , temporal frame alignments are computed using sliding window based dynamic programming method ; Third , robust SURF descriptors are employed to generate spatial frame alignments . Optical gas imaging ( OGI ) is one of the attractive methods to monitor hydrocarbon leak in the processing system . The manual analysis of a video frame to detect a potential leak is cumbersome and error-prone . The purpose of this study is to develop the automated hydrocarbon leak detection using appropriate technology and numerical technique . More and more non-computer major teachers have to use computer equipments in the video classroom to edit video ducuments on their own , This paper mainly introduces ways of video edition via for to enrich their teaching contents of multimedia . IPTV is well fit for digital home development and may make digital home applications more applicable . Breakthroughs in easy use and service innovation are expected to drive digital home forward , and , through cooperative devices , subscribers will be able to enjoy data , voice and video services all at the same time . Finally , community-based video podcasts help build instructor-to-peer and peer-to-peer connections within an online learning course . Future exploration on the design of video podcasts , regardless of the application used , is discussed . Interactive multimedia applications such as peer-to-peer P2P video services over the Internet have gained increasing popularity during the past few years . However , the adopted Internet-based P2P overlay network architecture hides the underlying network topology , assuming that channel quality is always in perfect condition . The measured power consumption is 103mW at 350MHz for 4 K encoding with high-quality mode . The 0.39nJ/pixel of energy efficiency of this work , which achieves 42 % ~ 97 % power reduction as compared with reference designs , make it ideal for real-time low-power smart video surveillance applications . The method specifically comprises the following steps of : ( 1 ) decoding an input high-resolution video code stream to extract a coded message , constituting multidimensional feature vectors to train a support vector machine model , and establishing a correlation between a high-resolution video coded message and an intra-frame encoding mode of a macroblock in a down-sizing video ; ( 2 ) constructing a layered SVM classifier to classify the intra-frame predicating modes of the macroblock in the down-sizing video , thereby reducing the number of the predicating modes ; ( 3 ) encoding the down-sizing video through the reduced intra-frame modes to realize the early termination of a rate-distortion optimization algorithm in the process of transcoding and recoding so as to achieve the purpose of reducing the complexity of the transcoding operation and realize a H.264 video code converter with low complexity ; and ( 4 ) outputting a low-resolution video code stream subjected to transcoding . The method can not only ensure the quality of the video after transcoding , but also effectively solve the problem on transcoding real-time property of the down-sized video . Describing Visual Content . Related to VQA are the tasks of image tagging [ reference ] , [ reference ] , image captioning [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] and video captioning [ reference ] , [ reference ] , where words or sentences are generated to describe visual content . While these tasks require both visual and semantic knowledge , captions can often be non - specific ( e.g. , observed by [ reference ] ) . This task can also be naturally cast as mapping an input sequence of words in a source document to a target sequence of words called summary . In the recent past , deep - learning based models that map an input sequence into another output sequence , called sequence - to - sequence models , have been successful in many problems such as machine translation , speech recognition and video captioning . In the framework of sequence - to - sequence models , a very relevant model to our task is the attentional Recurrent Neural Network ( RNN ) encoder - decoder model proposed in nmt , which has produced state - of - the - art performance in machine translation ( MT ) , which is also a natural language task . subsection : Facial Expression Recognition from Videos Traditionally , video - based recognition problems used per - frame features such as SIFT , dense - SIFT , HOG and recently deep features extracted with CNNs have been used . The per - frame features are then used to assign score to each individual frame . Contrary to the extraordinary development of high - quality semantic segmentation , research along the line to make semantic segmentation run fast while not sacrificing too much quality is left behind . We note actually this line of work is similarly important since it can inspire or enable many practical tasks in , for example , automatic driving , robotic interaction , online video processing , and even mobile computing where running time becomes a critical factor to evaluate system performance . Our experiments show that high - accuracy methods of ResNet38 and PSPNet take around 1 second to predict a high - resolution image on one Nvidia TitanX GPU card during testing .
In this paper , we propose a fast motion estimation technique using chroma interpolation filter for High Efficiency Video Coding ( HEVC ) encoder . In order to reduce the computation time of motion estimation process , the proposed method utilizes 4-tap chroma interpolation filter for motion estimation and 8-tap luma interpolation filter for motion compensation , separately , instead of applying the same 8-tap luma interpolation filter to both motion estimation and motion compensation process . From the experimental results , the proposed algorithm achieves 4.63 % time-saving with 0.06 % BD-bitrate increase on average , compared to x265 encoder , which is the most computationally efficient HEVC encoder in the industrial field .
Additionally , an optimization method based on the artificial bee colony ( ABC ) algorithm is proposed to improve the reconstruction performance . Using the proposed algorithm , the motion compensation problem for the single-RF-channel DBF array can be solved effectively , and the angle and Doppler information for the target can be simultaneously estimated . The effectiveness of the proposed algorithms is demonstrated by the results of numerical simulations .
We build on our prior work on best basis selection algorithms in large tree-structured dictionaries , previously used to construct effective image coders based on block and lapped transforms , as well as to improve the motion compensation step in video compression . In the present paper , we use such algorithms to select the optimal transform tiling for video compression .
Also , the mesh is coded as a tree and a sequence of motion vectors to ensure low bit rate . In the motion compensation part , using a modified affine transform that reduces the complexity of the motion compensation reduces the computational cost . The performance of this architecture is also presented .
A methodology to compare IR estimation with and without motion is presented . Based on this comparison , a method for motion effect compensation is proposed in order to reduce motion-induced distortions . The proposed methodology is applied to real data sets collected in 2007 by the Service Hydrographique et Océanographique de la Marine in a shallow water environment , proving its interest for motion effect analysis .
A methodology to compare the IR estimation in motionless and motion contexts is presented . Based on this comparison , a warping‐based lag‐Doppler filtering method in the wideband ambiguity plane for motion effect compensation is proposed . It aims at reducing the distortions due to the motion phenomena and accurately estimating the IR of the channel .
The paper assumes a working knowledge of MPEG-2 coding , the standard of which is now readily available . The results used in this paper were obtained using a straightforward set of software based on two programs : a motion vector estimator generating a file of best motion vectors for each macroblock and , an encoder program performing motion vector compensation , DCT coding , quantisation , runlength coding and entropy calculation .
In this paper , the implementation of H.263 real-time encoding on DSP-TM13XX is described . This implementation method can be easily extended to other standards such as H.263 + , MPEG-i , MPEG-2 , MPEG-4 etc . , because these standards are essentially very similar ( i.e. Motion Compensate , Discrete Cosine Transform , Quantization and Entropy Coding ) . In addition , TM13XX series of DSPs utilize a very powerful , general purpose VLIW ( Very Long Instruction Word ) processor core which makes them ideal for the high performance embedded multimedia applications .
This paper builds an aerocraft ' nonideal moiotn model , quantificationaUy analysed flight path data , estimates different aerocraft 's motion parameter . It is gained that the aerotransport best suits the SAR platform , the airship SAR 's motion compensation is the most difficult , unmanned aircraft 's motion characteristic has lots of differences , which motion compensation requirements should be particularly analysed .
When adapting a method from images to videos it is usually beneficial to incorporate the prior knowledge that frames of the same scene of a video can be approximated by a single image and a motion pattern . Estimating and compensating motion is a powerful mechanism to further constrain the problem and expose temporal correlations . It is therefore very common to find video SR methods that explicitly model motion through frames .
This nevertheless requires frame preprocessing and is usually expensive . Alternatively , motion compensation can also be performed jointly with the SR task , as done in the Bayesian approach of Liu et al . by iteratively estimating motion as part of its wider modeling of the downscaling process . The advent of neural network techniques that can be trained from data to approximate complex nonlinear functions has set new performance standards in many applications including SR .
The spatial transformer and SR modules are both differentiable and therefore end - to - end trainable . As a result , they can be jointly optimised to minimise a composite loss combining the accuracy of the reconstruction in eq : image - sr - objective with the fidelity of motion compensation in eq : motion - compensation - objective , namely section : Experiments and results
The models investigated are extended with a motion compensation mechanism based on spatial transformer networks that is efficient and jointly trainable for video SR . Results obtained with approaches that incorporate explicit motion compensation are demonstrated to be superior in terms of PSNR and temporal consistency compared to spatio - temporal models alone , and outperform the current state of the art in video SR . bibliography : References
This paper describes a video summarization and semantics editing tool that is suited for content-based video indexing and retrieval with appropriate human operator assistance . The whole system has been designed with a clear focus on the extraction and exploitation of motion information inherent in the dynamic video scene .
Indeed , experimental results show that a model-based algorithm outperforms state-of-the-art methods on predicting visual sentiment , visual realism and interestingness . Our paradigm can be used in various visual tasks ( e.g. , video summarization ) .
Experimentation done with the help of the test bed of videos has shown that higher codebook sizes of LBG have given better completeness in key frame extraction for video summarization . Experimental results are also discussed to represent the validity of the proposed method for video content summarization .
The existing methods can only extract the static images of videos as the content summarization , but they ignore the representation of motion information . To cope with these issues , a novel framework for an efficient video content summarization as well as video motion summarization is proposed . Initially , Capsules Net is trained as a spatiotemporal information extractor , and an inter-frames motion curve is generated based on those spatiotemporal features .
Based on the assumption that video features and textual features are highly correlated with each other , we can transfer textual information into knowledge on summarization using video information only . In particular , we formulate the video summarization problem as that of learning a mapping from a set of shots of a video to a subset of the shots using the general framework of SVM-based structured learning . Textual information is transferred by encoding them into a set of constraints used in the structured learning process which tend to provide a more detailed and accurate characterization of the different subsets of shots .
Video summarization is a simplification of video content for compacting the video information . The video summarization problem can be transformed to a clustering problem , in which some frames are selected to saliently represent the video content . In this work , we use a hierarchical graph-based clustering method for computing a video summary .
Most of the consecutive frames do have very little discriminative contents . In video summarization process , several frames containing similar information need to get processed , this leads to slower processing speed and higher complexity , consuming . Video more time summarization using key frames can ease the speed up of video processing .
We argue that video summarization should be an interactive , mixed-initiative process in which users have control over the summarization procedure while algorithms help users achieve their goal via video understanding . In this paper , we introduce ElasticPlay , a mixed-initiative approach that combines an advanced video summarization technique with direct interface manipulation to help users control the video summarization process . Users can specify a time budget for the remaining content while watching a video ; our system then immediately updates the playback plan using our proposed cut-and-forward algorithm , determining which parts to skip or to fast-forward .
This method can handle both symmetric and asymmetric group activities . Video based summarization have the potential to assist in maintaining public safety and
Efficiently access the record image of surveillance system is an important research goal . This paper proposes a simple method to enhance the video image summarization . The video summarization production process in this paper is divided into intrusion detection of moving objects and time axis with animation output .
An Eyetracking Study of How People View Digital Video Surrogates.- A State Transition Analysis of Image Search Patterns on the Web.- Towards a Comprehensive Survey of the Semantic Gap in Visual Image Retrieval.- Applications (Oral).- Audio-Based Event Detection for Sports Video.- Spectral Structuring of Home Videos.- Home Photo Retrieval : Time Matters.- Automatic Annotation of Tennis Action for Content-Based Retrieval by Integrated Audio and Visual Information.- Indexing of Personal Video Captured by a Wearable Imaging System.- Semantics/Learning II (Poster).- Constructive Learning Algorithm-Based RBF Network for Relevance Feedback in Image Retrieval.- Spatial-Temporal Semantic Grouping of Instructional Video Content.- A Novel Scheme for Video Similarity Detection.- Concept-Based Retrieval of Art Documents.- Video Retrieval II (Poster).- Video Retrieval of Human Interactions Using Model-Based Motion Tracking and Multi-layer Finite State Automata.- Fast Video Retrieval under Sparse Training Data.- Robust Content-Based Video Copy Identification in a Large Reference Database.- Video Summarization & Analysis (Poster).- ANSES : Summarisation of News Video.- Spatio-Temporal Decomposition of Sport Events for Video Indexing.- Audio-Assisted Scene Segmentation for Story Browsing.- Performance (Poster).- Performance Comparison of Different Similarity Models for CBIR with Relevance Feedback.- EBS k-d Tree :
Saliency prediction is valuable in many video applications , such as intelligent retrieval , advertisement design and delivering , video coding and video summarization generating . Although image saliency is well explored , less works have been done on videos .
Specifically , given a starting video frame , we first detect its salient regions based on extracted color and orientation maps ; then we track these regions for the rest of frames within a short period of time . The extracted salient region information could be used to evaluate the frame importance so as to provide guidance in keyframe extraction for video summarization purpose . Experiments on four test video sequences have yielded encouraging results .
Furthermore , the average prediction time taken by the proposed methodology is approximately 1.13 seconds which demonstrates the real-time monitoring efficiency of the system . The calculated outcomes show that the proposed methodology performs better contrasted with other contemporary studies for activity prediction , data processing cost , error rate , and time complexity .
While GTW treats different portions of a sequence as equally important , our task is in favor of early portions since an incomplete activity video always aligns from the beginning of a complete one . Thus motivated , we develop a temporally-weighted GTW ( TGTW ) algorithm for the activity prediction problem by encouraging alignment in the early portion of an activity sequence . Finally , the similarity derived from TGTW is combined with the k-nearest neighbors algorithm for predicting the activity class of an input sequence .
The current tools for proteolytic cleavage prediction have often relied purely on regular expressions , or involve models that do not represent biological data well . RESULTS A novel methodology for characterizing proteolytic cleavage site activities has been developed , which incorporates two fundamental features : activity class prediction and the use of an amino acid similarity matrix for ( non-parametric ) neural learning . The first solved the problem of predicting proteolytic efficiency . The second significantly improved the robustness in prediction and reduced the time complexity for learning . This study shows that activity class prediction is successful when applying this methodology to the prediction and characterization of Trypsin cleavage sites and the prediction of HIV protease cleavage sites . AVAILABILITY Requests for software and data should be made respectively to Dr Zheng Rong Yang and Miss Rebecca Thomson .
These approaches consider only information about each individual user and , thus , do not exploit the social connections and structure of the network . In this paper , we approach the problem of activity change prediction at the level of communities rather than individuals . We develop predictive models of activity change over communities obtained using state-of-art community detection methods and compare their predictive power with each other and against the single-user baseline and ego networks .
This communication puts forward a new approach for airport investment risk assessment . The approach takes explicitly into account the degree of uncertainty in activity levels prediction and proposes milestones for the different stages of the project for minimizing risk . Uncertainty is represented through fuzzy dual theory and risk management is performed using dynamic programming
In this paper , we present the various stages of Technological assistance and propose a new algorithm for the step of activities models detection . We also explore an activity prediction step using time series . The experiments were conducted on real data recorded at LIARA smart home and the results are satisfactory .
In this work , we introduce new QSAR models ( quantitative structure-activity relationship ) based on the GTM method ( generative topographic mapping ) , introduced by C. Bishop et al. A part of this thesis is dedicated to the visualization and analysis of large chemical libraries using the incremental version of GTM . We also introduce a new method coined “ Stargate GTM ” or S-GTM , which allows us to travel from the space of chemical descriptors to activity space and vice versa ; this approach was applied to activity profile prediction and inverse QSAR .
This paper proposes a model concerned with healthcare analytics at the individual level , which can predict human physical activity status from sequential lifelogging data collected from wearable sensors . The model has a two-stage hybrid structure ( in short , MOGP-HMM ) -- a multi-objective genetic programming ( MOGP ) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov model ( HMM ) in the second stage for activity status prediction over time . It can be used as a decision support tool to provide real-time monitoring , statistical analysis and personalized advice to individuals , encouraging positive attitudes towards healthy lifestyles .
Also , we provide an effective solution for the optimization problems . Moreover , we exploit the learned representations of users for the next activity type prediction . Finally , we present extensive experimental results to demonstrate the improved performances of the proposed method .
A prototype has been built to prove the feasibility of the proposed architecture and evaluate its performances . The results show that our proposed distributed video conferencing service can actually reduce the average bandwidth required for data and signaling messages at wireless mobile terminals while maintaining the main operations of a video conference session .
But since the football video screen changes rapidly , which increases the difficulty of segmentation . This paper first introduces five commonly used methods of shot segmentation , and then introduces three methods for soccer video segmentation , and finally gives a combination of methods used in soccer video segmentation . Hope that this article allows you to more quickly understand the methods of the shot segmentation about soccer videoes .
In this paper , we present a novel system and effective algorithms for soccer video segmentation . The output , about whether the ball is in play , reveals high-level structure of the content .
Any anti-piracy strategy requires not only copy detection but also precise frame alignment of pirate video with master content , prior to the estimation of geometric distortions and capture location in a theater . Most studies in pirate video registration focus on the alignment of watermarked sequences , while few efforts are made to align non-watermarked videos using content-based features . In this paper , we propose a spatio-temporal scheme for aligning pirate and master contents using visual features , which consists of three stages : First , a video sequence is compactly represented using 1-D SURF ( Speeded-Up Robust Features ) signatures ; Second , temporal frame alignments are computed using sliding window based dynamic programming method ; Third , robust SURF descriptors are employed to generate spatial frame alignments .
Optical gas imaging ( OGI ) is one of the attractive methods to monitor hydrocarbon leak in the processing system . The manual analysis of a video frame to detect a potential leak is cumbersome and error-prone . The purpose of this study is to develop the automated hydrocarbon leak detection using appropriate technology and numerical technique .
More and more non-computer major teachers have to use computer equipments in the video classroom to edit video ducuments on their own , This paper mainly introduces ways of video edition via for to enrich their teaching contents of multimedia .
IPTV is well fit for digital home development and may make digital home applications more applicable . Breakthroughs in easy use and service innovation are expected to drive digital home forward , and , through cooperative devices , subscribers will be able to enjoy data , voice and video services all at the same time .
Finally , community-based video podcasts help build instructor-to-peer and peer-to-peer connections within an online learning course . Future exploration on the design of video podcasts , regardless of the application used , is discussed .
Interactive multimedia applications such as peer-to-peer P2P video services over the Internet have gained increasing popularity during the past few years . However , the adopted Internet-based P2P overlay network architecture hides the underlying network topology , assuming that channel quality is always in perfect condition .
The measured power consumption is 103mW at 350MHz for 4 K encoding with high-quality mode . The 0.39nJ/pixel of energy efficiency of this work , which achieves 42 % ~ 97 % power reduction as compared with reference designs , make it ideal for real-time low-power smart video surveillance applications .
The method specifically comprises the following steps of : ( 1 ) decoding an input high-resolution video code stream to extract a coded message , constituting multidimensional feature vectors to train a support vector machine model , and establishing a correlation between a high-resolution video coded message and an intra-frame encoding mode of a macroblock in a down-sizing video ; ( 2 ) constructing a layered SVM classifier to classify the intra-frame predicating modes of the macroblock in the down-sizing video , thereby reducing the number of the predicating modes ; ( 3 ) encoding the down-sizing video through the reduced intra-frame modes to realize the early termination of a rate-distortion optimization algorithm in the process of transcoding and recoding so as to achieve the purpose of reducing the complexity of the transcoding operation and realize a H.264 video code converter with low complexity ; and ( 4 ) outputting a low-resolution video code stream subjected to transcoding . The method can not only ensure the quality of the video after transcoding , but also effectively solve the problem on transcoding real-time property of the down-sized video .
Describing Visual Content . Related to VQA are the tasks of image tagging [ reference ] , [ reference ] , image captioning [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] and video captioning [ reference ] , [ reference ] , where words or sentences are generated to describe visual content . While these tasks require both visual and semantic knowledge , captions can often be non - specific ( e.g. , observed by [ reference ] ) .
This task can also be naturally cast as mapping an input sequence of words in a source document to a target sequence of words called summary . In the recent past , deep - learning based models that map an input sequence into another output sequence , called sequence - to - sequence models , have been successful in many problems such as machine translation , speech recognition and video captioning . In the framework of sequence - to - sequence models , a very relevant model to our task is the attentional Recurrent Neural Network ( RNN ) encoder - decoder model proposed in nmt , which has produced state - of - the - art performance in machine translation ( MT ) , which is also a natural language task .
subsection : Facial Expression Recognition from Videos Traditionally , video - based recognition problems used per - frame features such as SIFT , dense - SIFT , HOG and recently deep features extracted with CNNs have been used . The per - frame features are then used to assign score to each individual frame .
Contrary to the extraordinary development of high - quality semantic segmentation , research along the line to make semantic segmentation run fast while not sacrificing too much quality is left behind . We note actually this line of work is similarly important since it can inspire or enable many practical tasks in , for example , automatic driving , robotic interaction , online video processing , and even mobile computing where running time becomes a critical factor to evaluate system performance . Our experiments show that high - accuracy methods of ResNet38 and PSPNet take around 1 second to predict a high - resolution image on one Nvidia TitanX GPU card during testing .
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