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Fast Video Segment Identification from Large Video Collection

In this paper we design a new global visual feature and use it as signature for “fast and dirty” video segment identification among video collection containing a large number of sequences. Different from previous key frame based shot representation, the proposed method combines both temporal-spatial and color range information in an appropriate way and could effectively and robustly characterizes video segments of varied length using a fixed-size 144-d signature. By using the global visual feature, the ambiguity of key frame selection and gradual shot transition problem could be avoided. Moreover, since the proposed feature could be extracted directly from MPEG compressed domains with low-cost process, it is also inexpensive to extract and acquire. Based on our experiment, by applying active search algorithm, the new global feature could support fast search: searching for a 15-sec clip among the 10.5 hours MPEG video database in merely 2 seconds. And it is also robust to color shifting and other video variations caused by digital devices.