We propose interest seam image, an efficient visual synopsis for video. To extract an interest seam image, a spatiotemporal energy map is constructed for the target video shot. Then an optimal seam which encompasses the highest energy is identified by an efficient dynamic programming algorithm. The optimal seam is used to extract a seam of pixels from each video frame to form one column of an image, based on which an interest seam image is finally composited. The interest seam image is efficient both in terms of computation and memory cost. Therefore it is able to power a wide variety of web-scale video content analysis applications, such as near duplicate video clip search, video genre recognition and classification, as well as video clustering, etc.. The representation capacity of the proposed interest seam image is demonstrated in a large scale video retrieval task. Its advantages are clearly exhibited when compared with previous works, as reported in our experiments.

[Reference]
http://videolectures.net/cvpr2010_hua_isi/



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