Abstract
Video shot boundary detection plays an every important role in video processing. It is the first step toward video content analysis and content-based video retrieval. We develop a novel approach for video shot boundary detection based on supervised learning. Our method consists in first extracting video frame feature using a supervised kernel non-locality preserving projections, then video frames are split into abrupt transitions, gradual transitions or normal frames using two cascaded Localized-SVM classifiers. Experimental results show the effectiveness of our method.
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