Video handling based on structured information for hypermedia systems
International conference on Multimedia information systems '91
C4.5: programs for machine learning
C4.5: programs for machine learning
Salient video stills: content and context preserved
MULTIMEDIA '93 Proceedings of the first ACM international conference on Multimedia
Image processing on compressed data for large video databases
MULTIMEDIA '93 Proceedings of the first ACM international conference on Multimedia
Automatic partitioning of full-motion video
Multimedia Systems
Video tomography: an efficient method for camerawork extraction and motion analysis
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Production model based digital video segmentation
Multimedia Tools and Applications
Video parsing and browsing using compressed data
Multimedia Tools and Applications
Video and image processing in multimedia systems
Video and image processing in multimedia systems
Automatic Video Indexing and Full-Video Search for Object Appearances
Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II
Performance Characterization and Comparison of Video Indexing Algorithms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A feature-based algorithm for detecting and classifying production effects
Multimedia Systems
A fast algorithm for video parsing using MPEG compressed sequences
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
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An approach for video segmentation into shots and sub-shots that works directly in the MPEG compressed domain is presented. It is based only on the information about macroblock coding mode and motion vectors in P and B frames. The system follows a two-pass scheme and has a hybrid rule-based/neural structure. A rough scan over the P frames locates the potential shot boundaries and the solution is then refined by a precise scan over the B frames of the respective neighborhoods. The “simpler” boundaries are recognized by the rule-based module, while the decisions for the “complex” ones are refined by the neural part. The latter is also used to distinguish dissolves from object and camera motions and to further divide shots into sub-shots. The experiments demonstrate high speed and classification accuracy without computationally expensive calculations and need for many thresholds.