In our case , a pfv corresponds to a Gaussian in the feature space of frames . We demonstrate that this representation is well suited for accurate video retrieval . The use of pfvs allows us to calculate confidence values for frames or sets of frames for being contained within a given video in the database . Aiming at the actual circumstance of narrow current ( network ) bandwidth , it 's impossible to deliver the video and search the video directly on the network . This(system ) gives a kind of way to search the video retrieval on the network using another design . It has friendly(user ) interface and good maintainability and expandability . In the last few years , the increase of online video has challenged research in the field of video information retrieval . Video search engines have become common on the Internet and require the use of powerful tools for fast access to data . Quantifying these differences is important for three reasons . The first is related to the necessity of understanding how users associate metadata to videos on YouTube , which is crucial for video information retrieval mechanisms and recommendation systems . The second is associated with understanding possible reasons that influence on the popularity of videos , essential to the association of advertisements to videos and performance issues related to the use of caches and CDNs . In this ( system ) , Luminance cumulative histogram is used to do rough retrieval , and then chrominance twice area ( dividing ) histogram is used to do fine retrieval . This system has high speed and accuracy , can be good used in ( lots ) ( of ) ( situations ) , such as VOD , video monitor recorder , video switch and video data retrieval , etc . It is a challenge work to describe a long video , it requires to control the granularity of the video ’s semantics , exclude redundancy information and give complete description . This task is very important for video understanding and video retrieving . In the paper , we proposed a framework to solve these problems . With the rapid development of Internet and multimedia technology , city public security video monitoringsystem produce a large number of video data every day , many problems need to be solved as video image retrieval , management and safety of the pressing demand . In general there are two points , one is the video retrieval accuracyproblem , the other is the issue of retrieval efficiency . However , up to now , three-dimensional ( 3D ) scene information has not been utilized to retrieve shots from a database consisting of monocular video sequences . In this paper , we propose the use of depth features for video retrieval purposes . Our depth feature extraction approach is based on a method that originally has been suggested to scan 3D objects with a single camera exploiting the motion parallax . Thus , the general film model providing this representation is considered third . In a way adequate to the idiosyncrasies of the film video data , the model serves as a basis for representing and querying the semantic and the structural multimodal content of films during the processes of video annotation and video visual retrieval through filtering . Furthermore , the temporal segmentation implemented in this study shows a direction towards semantic shot segmentation rather than common shot boundary detections based on statistical features . The possible improvements in video visual retrieval with proposed usersâ€ ™ visual ontology accompanied search mechanisms are also demonstrated with retrieval results . This nature of video search makes a path towards realisation of semantic video search . Tofuse thedifferent features ofsubtrajectories formore efficient andflexible retrieval , weusePASOMsasthesearching tool . Experimental results showthesuperior performance of theproposed approach forvideo retrieval comparing withprior approaches . I.INTRODUCTION Content-based video retrieval system hasgained muchatten- tion recently duetoits wideapplications suchasdigital library , newsbroadcasting , distant learning . Multimedia-content classification has been paramount in the last years , mainly because of the massive data accessed daily . Video-based retrieval and recommendation systems have attracted a considerable attention , since it is a profitable feature for several online and offline markets . In this work , we deal with the problem of automatic video classification in different genres based on visual information by means of Optimum-Path Forest ( OPF ) , which is a recently developed graph-based pattern recognition technique . The previous video track results demonstrated that it is far from trivial to take advantage of multiple modalities for the video retrieval search task . For almost any query , results based on ASR transcripts have been better than any other run . Provided is a display driver circuit ( 50 ) that performs frame interpolation and overshooting , wherein an interpolation frame generating unit ( 52 ) has a first generation mode in which to generate , on the basis of the images of key frames corresponding to video signals , interpolation frames obtained by changing the position of an object along with the passage of time , and a second generation mode in which to generate , on the basis of the images of the key frames , interpolation frames obtained by changing the display gray scale of an object along with the passage of time ; and an overshooting unit ( 53 ) applies different enhancement levels to gray scale transition for the interpolation frames which have been generated in the second generation mode , and to gray scale transition for the key frames . Thus , a configuration is proposed which enables improvement of the moving image response characteristic in a display driver circuit that performs frame interpolation and overshooting . Frame interpolation plays a crucial role in very low bit rate video coding , video format conversion , and slow motion replay . Motion-compensated interpolation is known to be the best solution for frame interpolation of video . In this work , three widely used motion-compensated algorithms for frame interpolation are compared and evaluated in terms of interpolated image quality , computation cost , and flexibility . The first algorithm considers motion occlusion , the second is the one used in the H.26L/JVT video codec , and the third uses bidirectional motion estimation . This paper presents a compact neural network for video frame interpolation using phase and amplitude feature pyramids . We design a set of one-dimensional separable complex Gabor filters to extract phase and amplitude feature pyramids for each input image , which is efficient and effective for motion representation . In the case of videos , epitomes are estimated so as to model most of the small space-time cubes from the input data . Then , the epitome can be used for various modeling and reconstruction tasks , of which we show results for video super-resolution , video interpolation , and object removal . Besides computational efficiency , an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data . This paper presents a new technique to improve the performance of video interpolation by controlling the subsampling rate of a video sequence . Simulation results show that the picture quality is enhanced and the quality fluctuation is reduced . Motion-compensated frame interpolation ( MCFI ) is widely used to smoothly display low frame rate video sequences by synthesizing and inserting new frames between existing frames . The temporal shift interpolation technique ( TSIT ) is popular for frame interpolation of video sequences that are encoded by a block-based video coding standard such as MPEG-4 or H.264/AVC . TSIT assumes the existence of a motion vector ( MV ) and may not result in high-quality interpolation for intra-mode blocks that do not have MVs . We propose a generative framework which takes on the video frame interpolation problem . Our framework , which we call Deep Locally Linear Embedding ( DeepLLE ) , is powered by a deep convolutional neural network ( CNN ) while it can be used instantly like conventional models . This paper presents a multi-frame pel-recursive two-dimensional motion estimation scheme for video image interpolation . The proposed approach uses more information from the image sequences than previous approaches , and gives a closed-form solution to update motion vectors among frame pairs simultaneously . We propose a novel approach for generating video interpolating frames from optical flow information . Our approach is based on solving an affine image-matching equation via the Green 's function method . This thesis presents a new method for video view interpolation using multiview linear camera arrays based on 2D domain triangulation . The domain of the reference image is initially partitioned into triangular regions using edge and scale information , aiming to place vertices along image edges and to increase the number of triangles in textured regions . Moreover , the proposed optimization framework is very general ; by designing different cut functions , we also discuss the extension of GFL to directed graphs . Exploiting the scalability of the proposed algorithm , we demonstrate the applications of our algorithm to the diagnosis of Alzheimer ’s disease ( AD ) and video background subtraction ( BS ) . In the AD problem , we formulated the diagnosis of AD as a GFL regularized classification . A general theory ( Group-RIP ) is developed for learning with structured sparsity , based on the notion of coding complexity associated with the structure , which guarantees better performance with more structure information other than pure sparsity . The new sparsity techniques under this framework have been successfully applied to different applications , such as compressive sensing MR imaging , video background subtraction , object tracking in visual surveillance , dynamic scene registration , automatic image annotation , medical image analysis , fast tag separation in cardiac tMRIs , cervigram image segmentation and so on . The improved experimental results in these applications further validate the nice theoretical guarantees of our new theorems and demonstrate the effectiveness of our new framework in the practical applications involved large scale data . We adopt a convolutional long short-term memory ( LSTM ) into the encoder-decoder architecture , which successfully learns the spatiotemporal relation with relatively a small number of parameters . The proposed network is applied to one of the dense pixel-prediction problems , specifically , the background subtraction in video sequences . The proposed network is trained with limited duration of video frames , and yet it shows good generalization performance for different videos and time duration . Mixture of Gaussians ( MoG ) modelling [ 13 ] is a popular approach to background subtraction in video sequences . Although the algorithm shows good empirical performance , it lacks theoretical justification . Background subtraction in video provides the preliminary information essential for many computer vision applications . In this paper , we propose a sequence of approaches named CANDID to solve the change detection problem in challenging video scenarios . Using visual saliency map for modelling gives important information for understanding in many applications . In this paper we present a simple method with low computation load using visual saliency map for background subtraction in video stream . The proposed technique is based on finding image segments whose intensity values can be distinguished accurate . This paper deals with an efficient background subtraction of image/frames of video by improving the execution speed , accuracy and reduce the usage of memory . Three important techniques are applied to improve the efficiency : superpixel extraction , canny edge detection and fuzzy c means . Background subtraction video segmentation is the important first step for video surveillance applications with fixed camera . There are many existing methods in the literature . We propose a factorized robust matrix completion ( FRMC ) algorithm with global motion compensation to solve the video background subtraction problem . The algorithm decomposes a sequence of video frames into the sum of a low rank background component and a sparse motion component . In cluster video servers , one of the important issues is how to store multimedia data on many storage nodes . This issue includes two topics : the scheme of splitting an entire file into many clips , and the storage of these clips on many nodes . For cluster video servers , it is very important to design a good distributed storage system with high performance . One of the important issues in designing a good distributed storage system is how to store multimedia data on many storage nodes . The monitor incorporates all requirement of the technology . Features include : video EEG acquisition , online data reviewing during acquisition , online quantitative processing of EEG/EKG/PO2 signals , online trends display of all computed parameters ( ex : aEEG , DSA , AP , RP , frequency indexes , BSI , SI among others ) , easy annotation entry , easy access to raw data from trend displays , customizable screen display for edition/reviewing , visual alarms , artifact detection during acquisition , customized montages and protocols , standardized report of ICU EEG according to ACNS guidelines , configurable notification for remote users . Conclusions In this method , a novel algorithm is proposed for generating the supervoxel . In 3D video volume , motion consistence of moving object is a frequent challenge for segmentation algorithm and a robust , long-term supervoxel could help in this situation . The segmentation process is considered as a minimization of an energy function where variables are labels of supervoxels . Specifically , autoencoder based learned image or video compression solutions are emerging as strong competitors to traditional approaches . In this work , We propose a new network architecture , based on common and well studied components , for learned video compression operating in low latency mode . Our method yields state of the art MS-SSIM/rate performance on the high-resolution UVG dataset , among both learned video compression approaches and classical video compression methods ( H.265 and H.264 ) in the rate range of interest for streaming applications . This paper presents a novel method for supporting full interactive video applications over wireless networks . A major limitation in wireless networks is that mobile users must rely on a battery with a limited supply of energy . The realtime denoise of video images is a modern field of signal processing . In the paper , the intelligent denoise algorithm is carried out . We introduced case studies and video conferencing-based projects to supplement lectures in teaching a graduate level telecommunications management course . The effectiveness of these methods in improving learning effectiveness was measured by administering questionnaires to students . The diversity in TV images has augmented with the increased application of computer graphics . In this paper we study z coding system that supports both the lossless coding of such graphics data and regular lossy video compression . The lossless coding techniques are based on runlength and arithmetical coding . HERO encodes multimodal inputs in a hierarchical fashion , where local textual context of a video frame is captured by a Cross-modal Transformer via multimodal fusion , and global video context is captured by a Temporal Transformer . Besides standard Masked Language Modeling ( MLM ) and Masked Frame Modeling ( MFM ) objectives , we design two new pre-training tasks : ( i ) Video-Subtitle Matching ( VSM ) , where the model predicts both global and local temporal alignment ; and ( ii ) Frame Order Modeling ( FOM ) , where the model predicts the right order of shuffled video frames . Different from previous work that mostly focused on cooking or narrated instructional videos , HERO is jointly trained on HowTo100 M and large-scale TV show datasets to learn complex social scenes , dynamics backdrop transitions and multi-character interactions . This paper introduces a new approach to using multicamera images of an event to provide a free-viewpoint video visualization of sport scenes . This allows a virtual camera to be moved freely around , and to view the action from any angle .
In our case , a pfv corresponds to a Gaussian in the feature space of frames . We demonstrate that this representation is well suited for accurate video retrieval . The use of pfvs allows us to calculate confidence values for frames or sets of frames for being contained within a given video in the database .
Aiming at the actual circumstance of narrow current ( network ) bandwidth , it 's impossible to deliver the video and search the video directly on the network . This(system ) gives a kind of way to search the video retrieval on the network using another design . It has friendly(user ) interface and good maintainability and expandability .
In the last few years , the increase of online video has challenged research in the field of video information retrieval . Video search engines have become common on the Internet and require the use of powerful tools for fast access to data .
Quantifying these differences is important for three reasons . The first is related to the necessity of understanding how users associate metadata to videos on YouTube , which is crucial for video information retrieval mechanisms and recommendation systems . The second is associated with understanding possible reasons that influence on the popularity of videos , essential to the association of advertisements to videos and performance issues related to the use of caches and CDNs .
In this ( system ) , Luminance cumulative histogram is used to do rough retrieval , and then chrominance twice area ( dividing ) histogram is used to do fine retrieval . This system has high speed and accuracy , can be good used in ( lots ) ( of ) ( situations ) , such as VOD , video monitor recorder , video switch and video data retrieval , etc .
It is a challenge work to describe a long video , it requires to control the granularity of the video ’s semantics , exclude redundancy information and give complete description . This task is very important for video understanding and video retrieving . In the paper , we proposed a framework to solve these problems .
With the rapid development of Internet and multimedia technology , city public security video monitoringsystem produce a large number of video data every day , many problems need to be solved as video image retrieval , management and safety of the pressing demand . In general there are two points , one is the video retrieval accuracyproblem , the other is the issue of retrieval efficiency .
However , up to now , three-dimensional ( 3D ) scene information has not been utilized to retrieve shots from a database consisting of monocular video sequences . In this paper , we propose the use of depth features for video retrieval purposes . Our depth feature extraction approach is based on a method that originally has been suggested to scan 3D objects with a single camera exploiting the motion parallax .
Thus , the general film model providing this representation is considered third . In a way adequate to the idiosyncrasies of the film video data , the model serves as a basis for representing and querying the semantic and the structural multimodal content of films during the processes of video annotation and video visual retrieval through filtering .
Furthermore , the temporal segmentation implemented in this study shows a direction towards semantic shot segmentation rather than common shot boundary detections based on statistical features . The possible improvements in video visual retrieval with proposed usersâ€ ™ visual ontology accompanied search mechanisms are also demonstrated with retrieval results . This nature of video search makes a path towards realisation of semantic video search .
Tofuse thedifferent features ofsubtrajectories formore efficient andflexible retrieval , weusePASOMsasthesearching tool . Experimental results showthesuperior performance of theproposed approach forvideo retrieval comparing withprior approaches . I.INTRODUCTION Content-based video retrieval system hasgained muchatten- tion recently duetoits wideapplications suchasdigital library , newsbroadcasting , distant learning .
Multimedia-content classification has been paramount in the last years , mainly because of the massive data accessed daily . Video-based retrieval and recommendation systems have attracted a considerable attention , since it is a profitable feature for several online and offline markets . In this work , we deal with the problem of automatic video classification in different genres based on visual information by means of Optimum-Path Forest ( OPF ) , which is a recently developed graph-based pattern recognition technique .
The previous video track results demonstrated that it is far from trivial to take advantage of multiple modalities for the video retrieval search task . For almost any query , results based on ASR transcripts have been better than any other run .
Provided is a display driver circuit ( 50 ) that performs frame interpolation and overshooting , wherein an interpolation frame generating unit ( 52 ) has a first generation mode in which to generate , on the basis of the images of key frames corresponding to video signals , interpolation frames obtained by changing the position of an object along with the passage of time , and a second generation mode in which to generate , on the basis of the images of the key frames , interpolation frames obtained by changing the display gray scale of an object along with the passage of time ; and an overshooting unit ( 53 ) applies different enhancement levels to gray scale transition for the interpolation frames which have been generated in the second generation mode , and to gray scale transition for the key frames . Thus , a configuration is proposed which enables improvement of the moving image response characteristic in a display driver circuit that performs frame interpolation and overshooting .
Frame interpolation plays a crucial role in very low bit rate video coding , video format conversion , and slow motion replay . Motion-compensated interpolation is known to be the best solution for frame interpolation of video . In this work , three widely used motion-compensated algorithms for frame interpolation are compared and evaluated in terms of interpolated image quality , computation cost , and flexibility . The first algorithm considers motion occlusion , the second is the one used in the H.26L/JVT video codec , and the third uses bidirectional motion estimation .
This paper presents a compact neural network for video frame interpolation using phase and amplitude feature pyramids . We design a set of one-dimensional separable complex Gabor filters to extract phase and amplitude feature pyramids for each input image , which is efficient and effective for motion representation .
In the case of videos , epitomes are estimated so as to model most of the small space-time cubes from the input data . Then , the epitome can be used for various modeling and reconstruction tasks , of which we show results for video super-resolution , video interpolation , and object removal . Besides computational efficiency , an interesting advantage of the epitome as a representation is that it can be reliably estimated even from videos with large amounts of missing data .
This paper presents a new technique to improve the performance of video interpolation by controlling the subsampling rate of a video sequence . Simulation results show that the picture quality is enhanced and the quality fluctuation is reduced .
Motion-compensated frame interpolation ( MCFI ) is widely used to smoothly display low frame rate video sequences by synthesizing and inserting new frames between existing frames . The temporal shift interpolation technique ( TSIT ) is popular for frame interpolation of video sequences that are encoded by a block-based video coding standard such as MPEG-4 or H.264/AVC . TSIT assumes the existence of a motion vector ( MV ) and may not result in high-quality interpolation for intra-mode blocks that do not have MVs .
We propose a generative framework which takes on the video frame interpolation problem . Our framework , which we call Deep Locally Linear Embedding ( DeepLLE ) , is powered by a deep convolutional neural network ( CNN ) while it can be used instantly like conventional models .
This paper presents a multi-frame pel-recursive two-dimensional motion estimation scheme for video image interpolation . The proposed approach uses more information from the image sequences than previous approaches , and gives a closed-form solution to update motion vectors among frame pairs simultaneously .
We propose a novel approach for generating video interpolating frames from optical flow information . Our approach is based on solving an affine image-matching equation via the Green 's function method .
This thesis presents a new method for video view interpolation using multiview linear camera arrays based on 2D domain triangulation . The domain of the reference image is initially partitioned into triangular regions using edge and scale information , aiming to place vertices along image edges and to increase the number of triangles in textured regions .
Moreover , the proposed optimization framework is very general ; by designing different cut functions , we also discuss the extension of GFL to directed graphs . Exploiting the scalability of the proposed algorithm , we demonstrate the applications of our algorithm to the diagnosis of Alzheimer ’s disease ( AD ) and video background subtraction ( BS ) . In the AD problem , we formulated the diagnosis of AD as a GFL regularized classification .
A general theory ( Group-RIP ) is developed for learning with structured sparsity , based on the notion of coding complexity associated with the structure , which guarantees better performance with more structure information other than pure sparsity . The new sparsity techniques under this framework have been successfully applied to different applications , such as compressive sensing MR imaging , video background subtraction , object tracking in visual surveillance , dynamic scene registration , automatic image annotation , medical image analysis , fast tag separation in cardiac tMRIs , cervigram image segmentation and so on . The improved experimental results in these applications further validate the nice theoretical guarantees of our new theorems and demonstrate the effectiveness of our new framework in the practical applications involved large scale data .
We adopt a convolutional long short-term memory ( LSTM ) into the encoder-decoder architecture , which successfully learns the spatiotemporal relation with relatively a small number of parameters . The proposed network is applied to one of the dense pixel-prediction problems , specifically , the background subtraction in video sequences . The proposed network is trained with limited duration of video frames , and yet it shows good generalization performance for different videos and time duration .
Mixture of Gaussians ( MoG ) modelling [ 13 ] is a popular approach to background subtraction in video sequences . Although the algorithm shows good empirical performance , it lacks theoretical justification .
Background subtraction in video provides the preliminary information essential for many computer vision applications . In this paper , we propose a sequence of approaches named CANDID to solve the change detection problem in challenging video scenarios .
Using visual saliency map for modelling gives important information for understanding in many applications . In this paper we present a simple method with low computation load using visual saliency map for background subtraction in video stream . The proposed technique is based on finding image segments whose intensity values can be distinguished accurate .
This paper deals with an efficient background subtraction of image/frames of video by improving the execution speed , accuracy and reduce the usage of memory . Three important techniques are applied to improve the efficiency : superpixel extraction , canny edge detection and fuzzy c means .
Background subtraction video segmentation is the important first step for video surveillance applications with fixed camera . There are many existing methods in the literature .
We propose a factorized robust matrix completion ( FRMC ) algorithm with global motion compensation to solve the video background subtraction problem . The algorithm decomposes a sequence of video frames into the sum of a low rank background component and a sparse motion component .
In cluster video servers , one of the important issues is how to store multimedia data on many storage nodes . This issue includes two topics : the scheme of splitting an entire file into many clips , and the storage of these clips on many nodes .
For cluster video servers , it is very important to design a good distributed storage system with high performance . One of the important issues in designing a good distributed storage system is how to store multimedia data on many storage nodes .
The monitor incorporates all requirement of the technology . Features include : video EEG acquisition , online data reviewing during acquisition , online quantitative processing of EEG/EKG/PO2 signals , online trends display of all computed parameters ( ex : aEEG , DSA , AP , RP , frequency indexes , BSI , SI among others ) , easy annotation entry , easy access to raw data from trend displays , customizable screen display for edition/reviewing , visual alarms , artifact detection during acquisition , customized montages and protocols , standardized report of ICU EEG according to ACNS guidelines , configurable notification for remote users . Conclusions
In this method , a novel algorithm is proposed for generating the supervoxel . In 3D video volume , motion consistence of moving object is a frequent challenge for segmentation algorithm and a robust , long-term supervoxel could help in this situation . The segmentation process is considered as a minimization of an energy function where variables are labels of supervoxels .
Specifically , autoencoder based learned image or video compression solutions are emerging as strong competitors to traditional approaches . In this work , We propose a new network architecture , based on common and well studied components , for learned video compression operating in low latency mode . Our method yields state of the art MS-SSIM/rate performance on the high-resolution UVG dataset , among both learned video compression approaches and classical video compression methods ( H.265 and H.264 ) in the rate range of interest for streaming applications .
This paper presents a novel method for supporting full interactive video applications over wireless networks . A major limitation in wireless networks is that mobile users must rely on a battery with a limited supply of energy .
The realtime denoise of video images is a modern field of signal processing . In the paper , the intelligent denoise algorithm is carried out .
We introduced case studies and video conferencing-based projects to supplement lectures in teaching a graduate level telecommunications management course . The effectiveness of these methods in improving learning effectiveness was measured by administering questionnaires to students .
The diversity in TV images has augmented with the increased application of computer graphics . In this paper we study z coding system that supports both the lossless coding of such graphics data and regular lossy video compression . The lossless coding techniques are based on runlength and arithmetical coding .
HERO encodes multimodal inputs in a hierarchical fashion , where local textual context of a video frame is captured by a Cross-modal Transformer via multimodal fusion , and global video context is captured by a Temporal Transformer . Besides standard Masked Language Modeling ( MLM ) and Masked Frame Modeling ( MFM ) objectives , we design two new pre-training tasks : ( i ) Video-Subtitle Matching ( VSM ) , where the model predicts both global and local temporal alignment ; and ( ii ) Frame Order Modeling ( FOM ) , where the model predicts the right order of shuffled video frames . Different from previous work that mostly focused on cooking or narrated instructional videos , HERO is jointly trained on HowTo100 M and large-scale TV show datasets to learn complex social scenes , dynamics backdrop transitions and multi-character interactions .
This paper introduces a new approach to using multicamera images of an event to provide a free-viewpoint video visualization of sport scenes . This allows a virtual camera to be moved freely around , and to view the action from any angle .
Effective and Efficient Indexing for Large Video Databases Design and research of Web video retrieval system based on MVC pattern An efficient indexing structure for multimedia data A contextual analysis of the YouTube duplicate content Real-time classification video retrieval system based on HSI model Multi Semantic Feature Fusion Framework for Video Segmentation and Description Huge Amounts of Video Image Retrieval Technology Research Based on Map/Reduce Using depth features to retrieve monocular video shots Spatio-temporal synchronization and semantic modeling for video and multimedia database systems Video visual feature space reduction and semantic search for visual content retrieval A MotionTrajectory BasedVideoRetrieval System UsingParallel Adaptive SelfOrganizing Maps Supervised Video Genre Classification Using Optimum-Path Forest Combining Information Sources for Video Retrieval Display driver circuit, liquid crystal display device, display driving method, control program, and computer-readable recording medium having same control program recorded therein Comparison of motion-compensated algorithms for frame interpolation Frame Interpolation Using Phase and Amplitude Feature Pyramids Video Epitomes A new video interpolation technique based on motion-adaptive subsampling Motion-Compensated Frame Interpolation for Intra-Mode Blocks Video Frame Interpolation by Plug-and-Play Deep Locally Linear Embedding Multi-frame pel-recursive motion estimation for video image interpolation Video interpolation through Green's functions of matching equations Video view interpolation using temporally adaptive 3D meshes Efficient Generalized Fused Lasso and Its Applications Structured sparsity: theorems, algorithms and applications Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification Regularized online Mixture of Gaussians for background subtraction CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction A Novel Approach to Background Subtraction Using Visual Saliency Map Improving the Efficiency of Background Subtraction using Superpixel Extraction and Midpoint for Centroid Real-Time Memory-Efficient Video Object Segmentation in Dynamic Background with Multi-Background Registration Technique Video background subtraction using semi-supervised robust matrix completion Owl: a new multimedia data splitting scheme for cluster video server A new distributed storage scheme for cluster video server 56. Continuous EEG monitoring at the emergency and intensive care unit: Presentation of a Cuban solution Video object segmentation based on supervoxel for multimedia corpus construction Feedback Recurrent Autoencoder for Video Compression MPEG-4 interactive video streaming over wireless networks Intelligent noise reduction algorithm and software Video-conferencing in a classroom Compression of mixed video and graphics images for TV systems HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training A Free-Viewpoint Video System for Visualization of Sport Scenes
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2007 2006 2008 2009 2004 2016 2015 2007 1998 2011 2005 2015 2003 2010 2003 2019 2008 1999 2008 2018 1994 2005 2015 2016 2011 2018 2011 2018 2015 2012 2007 2014 2002 2005 2016 2016 2020 2005 2003 1996 1998 2020 2007
BTW Multimedia Information Retrieval WebMedia CIVR CIARP TRECVID ICIP ICIP (2) ICIP (3) ICPR AVSS MMSP ICASSP EUROMICRO
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