17 December 1998 Video segmentation and classification for content-based storage and retrieval using motion vectors
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Abstract
Video parsing is an important step in content-based indexing techniques, where the input video is decomposed into segments with uniform content. In video parsing detection of scene changes is one of the approaches widely used for extracting key frames from the video sequence. In this paper, an algorithm, based on motion vectors, is proposed to detect sudden scene changes and gradual scene changes (camera movements such as panning, tilting and zooming). Unlike some of the existing schemes, the proposed scheme is capable of detecting both sudden and gradual changes in uncompressed, as well as, compressed domain video. It is shown that the resultant motion vector can be used to identify and classify gradual changes due to camera movements. Results show that algorithm performed as well as the histogram-based schemes, with uncompressed video. The performance of the algorithm was also investigated with H.263 compressed video. The detection and classification of both sudden and gradual scene changes was successfully demonstrated.
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W.A.C. Fernando, Cedric Nishan Canagarajah, David R. Bull, "Video segmentation and classification for content-based storage and retrieval using motion vectors", Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); doi: 10.1117/12.333889; https://doi.org/10.1117/12.333889
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