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Shot boundary detection using unsupervised clustering and hypothesis testing

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4 Author(s)
Hong Lu ; Sch. of Inf. Sci. & Eng., Fudan Univ., Shanghai, China ; Yap-Peng Tan ; Xiangyang Xue ; Lide Wu

We propose a shot boundary detection approach based on unsupervised scenelet clustering and hypothesis testing. We define a video scenelet as a short consecutive samples of frames of a video sequence. The approach makes use of a typical k-means clustering algorithm to group the scenelets. Based on the clustering result, hypothesis testing can be performed to identify the shot boundaries at each level with a different cluster number. Combined with a cluster validity analysis to decide a suitable number of clusters, promising results can be obtained for shot boundary detection.

Published in:

Communications, Circuits and Systems, 2004. ICCCAS 2004. 2004 International Conference on  (Volume:2 )

Date of Conference:

27-29 June 2004