Abstract
Video Shot Detection and Summarization plays a vital role in analyzing the contents of the video. The algorithms and methodologies learnt from video shot detection and summarization have a wide range of applications starting from video browsing, content-based video retrieval and storage, surveillance and many more. In an earlier work [1], we have extracted six texture features using gray level co-occurrence matrix, one of the most popular texture feature extraction methods. Frames from input video sequence are converted in texture domain. These video sequences are used to determine the “CUT” transition. In the proposed work, we have used GLCM and texture spectrum to extract texture features from frames in video and used a simple video shot detection method to find “CUT” transition and analyzed the results using quality metric parameters to determine the best feature extractor among GLCM and texture spectrum. The clustering algorithm for video summarization is affinity propagation.
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