Novel video indexing approach based on people-similarity

This paper presents a novel approach to video indexing. In this approach, we define a people-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 whether they belong to an identical person. The extended support vector machines are applied to map a serial of low-level feature distances to a perceived people-similarity. To build people-similarity based video indexing, a novel unsupervised clustering algorithm is also proposed, which can more correctly group individual person according to the mutual people-similarities among multiple people pairs.