Automatic partitioning of full-motion video
Multimedia Systems
Machine Learning
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A feature-based algorithm for detecting and classifying production effects
Multimedia Systems
Temporal segmentation of video using frame and histogram space
IEEE Transactions on Multimedia
Statistical models of video structure for content analysis and characterization
IEEE Transactions on Image Processing
Motion analysis and segmentation through spatio-temporal slices processing
IEEE Transactions on Image Processing
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
A unified approach to shot change detection and camera motion characterization
IEEE Transactions on Circuits and Systems for Video Technology
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
Video partitioning by temporal slice coherency
IEEE Transactions on Circuits and Systems for Video Technology
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
Video shot segmentation using graph-based dominant-set clustering
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Efficient temporal segmentation for sports programs with special cases
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Automatic and fast temporal segmentation for personalized news consuming
Information Systems Frontiers
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In this paper, we propose a novel approach for the robust detection and classification of dissolve sequences in videos. Our approach is based on the multi-resolution representation of temporal slices extracted from 3D image volume. At the low-resolution (LR) scale, the problem of dissolve detection is reduced as cut transition detection. At the high-resolution (HR) space, Gabor wavelet features are computed for regions that surround the cuts located at LR scale. The computed features are then input to support vector machines for pattern classification. Encouraging results have been obtained through experiments.