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
Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines
Pattern Recognition Letters
Adaptive edge-oriented shot boundary detection
Journal on Image and Video Processing
Video shot boundary detection: Seven years of TRECVid activity
Computer Vision and Image Understanding
A unified model for techniques on video-shot transition detection
IEEE Transactions on Multimedia
Video Segmentation via Temporal Pattern Classification
IEEE Transactions on Multimedia
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
A Formal Study of Shot Boundary Detection
IEEE Transactions on Circuits and Systems for Video Technology
Linear Transition Detection as a Unified Shot Detection Approach
IEEE Transactions on Circuits and Systems for Video Technology
A Model-Based Shot Boundary Detection Technique Using Frame Transition Parameters
IEEE Transactions on Multimedia
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In this paper, we present a robust and efficient approach which is capable to simultaneously detect various shot boundaries in a unified way. The proposed approach first detects general shot boundaries based on the idea of Fisher criterion, and then classifies them into two categories, cut and gradual transition (GT), by an SVM classifier. Further computation is performed for the GT shot boundaries to expand rough boundary locations between two frames into the transition interval consisting of all the transitional frames. Finally, a postprocessing module is employed to merge overlapped transitions. The evaluation experiments show that the proposed approach has the impressive performance in both efficiency and accuracy, and outperforms the best results of all the participants of TRECVID 2006.