Exploiting the JPEG Compression Scheme for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring functionalities in the compressed image/video domain
ACM Computing Surveys (CSUR)
VideoTrails: representing and visualizing structure in video sequences
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
A mask matching approach for video segmentation on compressed data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent multimedia computing and networking
IRIS - a system for image and video retrieval
CASCON '96 Proceedings of the 1996 conference of the Centre for Advanced Studies on Collaborative research
Performance Characterization and Comparison of Video Indexing Algorithms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Synchronization of multiple video recordings based on still camera flashes
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A narrative-based abstraction framework for story-oriented video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
An unified transition detection based on bipartite graph matching approach
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Compressed telesurveillance video database retrieval using fuzzy classification system
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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A common framework for rapid scene analysis for detecting scene changes in compressed Motion JPEG and MPEG videos is proposed. We develop algorithms to detect both abrupt and gradual scene changes. The algorithms operate directly on the DC sequence which can be easily extracted from Motion JPEG and MPEG compressed video without decompression. The DC images capture most of the essential "global" information, but is of a small fraction of the original data size. Operating on these images offers significant computation savings. Experimental results show that the proposed algorithms are fast and effective in detecting abrupt scene changes and gradual transitions.