FAM-Based Fuzzy Inference for Detecting Shot Transitions
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Detecting Shot Transitions for Video Indexing with FAM
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Online Bayesian Video Summarization and Linking
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Reducing false positives in video shot detection using learning techniques
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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We describe a new approach to the detection and classification of scene breaks in video sequences. Our method can detect and classify a variety of scene breaks, including cuts, fades, dissolves and wipes, even in sequences involving significant motion. We detect the appearance of intensity edges that are distant from edges in the previous frame. A global motion computation is used to handle camera or object motion. The algorithms we propose withstand compression artifacts such as those introduced by JPEG and MPEG, even at very high compression rates. Experimental evidence demonstrates that our method can detect and classify scene breaks that are difficult to detect with previous approaches. An initial implementation runs at approximately 2 frames per second on a Sun workstation.