Unsupervised clustering algorithm for video shots using spectral division

Abstract

A new unsupervised clustering algorithm, Spectral-division Unsuper-vised Shot-clustering Algorithm (SUSC), is proposed in this paper. Key-fames are picked out to represent the shots, and color feature of key-frames are extracted to describe video shots. Spherical Gaussian Model (SGM) is constructed for every shot category to form effective descriptions of them. Then Spectral Division (SD) method is employed to divide a category into two categories, and the method is iteratively used for further divisions. After each iterative shot-division, Bayesian information Criterion (BIC) is utilized to automatically judge whether to stop further division. During this processes, one category may be dissevered by mistake. In order to correct these mistakes, similar categories will be merged by calculating the similarities of every two categories. This approach is applied to three kinds of sports videos, and the experimental results show that the proposed approach is reliable and effective. © Springer-Verlag Berlin Heidelberg 2008.