A people similarity based approach to video indexing

This paper presents a new approach to people-based video indexing. In this approach, we define a people-based similarity measure according to both clothing similarity and speaking voice similarity. Such similarity depicts how perceptually similar two people appearing in different scenes are and if they belong to an identical person. Instead of computing in feature space, the proposed people-based similarity is computed in distance space. The extended Support Vector Machines (SVMs) are employed to map a serial of low-level feature distances to a perceived people similarity. In order to build people-based video indexing, a novel unsupervised clustering algorithm is also proposed, which can more correctly identify individual person according to mutual people similarities between two people. The experiments on large video testing data have demonstrated the effectiveness and efficiency of the proposed people-based similarity, unsupervised clustering and video indexing.