Hierarchical Shot Clustering for Video Summarization
ICCS '02 Proceedings of the International Conference on Computational Science-Part III
Non-sequential multiscale content-based video decomposition
Signal Processing - Special section on content-based image and video retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Video personalization in resource-constrained multimedia environments
Proceedings of the 15th international conference on Multimedia
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Multimedia Tools and Applications
Hierarchical modeling and adaptive clustering for real-time summarization of rush videos
IEEE Transactions on Multimedia
An automatic web-oriented multimedia extraction and multiresolution visualization scheme
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
Adaptive key frame extraction for video summarization using an aggregation mechanism
Journal of Visual Communication and Image Representation
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An efficient technique for summarization of stereoscopic video sequences is presented, which extracts a small but meaningful set of video frames using a content-based sampling algorithm. The proposed video-content representation provides the capability of browsing digital stereoscopic video sequences and performing more efficient content-based queries and indexing. Each stereoscopic video sequence is first partitioned into shots by applying a shot-cut detection algorithm so that frames (or stereo pairs) of similar visual characteristics are gathered together. Each shot is then analyzed using stereo-imaging techniques, and the disparity field, occluded areas, and depth map are estimated. A multiresolution implementation of the recursive shortest spanning tree (RSST) algorithm is applied for color and depth segmentation, while fusion of color and depth segments is employed for reliable video object extraction. In particular, color segments are projected onto depth segments so that video objects on the same depth plane are retained, while at the same time accurate object boundaries are extracted. Feature vectors are then constructed using multidimensional fuzzy classification of segment features including size, location, color, and depth. Shot selection is accomplished by clustering similar shots based on the generalized Lloyd-Max algorithm, while for a given shot, key frames are extracted using an optimization method for locating frames of minimally correlated feature vectors. For efficient implementation of the latter method, a genetic algorithm is used. Experimental results are presented, which indicate the reliable performance of the proposed scheme on real-life stereoscopic video sequences