Automatic partitioning of full-motion video
Multimedia Systems
A feature-based algorithm for detecting and classifying scene breaks
Proceedings of the third ACM international conference on Multimedia
Efficient and cost-effective techniques for browsing and indexing large video databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Combined Audio and Visual Streams Analysis for Video Sequence Segmentation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
On the extraction of DC sequence from MPEG compressed video
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Video summarization based on user log enhanced link analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Super-resolution reconstruction of compressed video using transform-domain statistics
IEEE Transactions on Image Processing
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
Scene extraction in motion pictures
IEEE Transactions on Circuits and Systems for Video Technology
Optimal content-based video decomposition for interactive video navigation
IEEE Transactions on Circuits and Systems for Video Technology
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This paper describes a fully automatic content-based approach for browsing and retrieval of MPEG-2 compressed video. The first step of the approach is the detection of shot boundaries based on motion vectors available from the compressed video stream. The next step involves the construction of a scene tree from the shots obtained earlier. The scene tree is shown to capture some semantic information as well as provide a construct for hierarchical browsing of compressed videos. Finally, we build a new model for video similarity based on global as well as local motion associated with each node in the scene tree. To this end, we propose new approaches to camera motion and object motion estimation. The experimental results demonstrate that the integration of the above techniques results in an efficient framework for browsing and searching large video databases.