Object-based video abstraction using cluster analysis

Changick Kim, Jenq-Neng Hwang

Abstract: Among various semantic primitives of video, objects of interest along with their actions and generated events can play an important role in some applications such as an object-based video surveillance system and an object based video indexing/retrieval system. In this paper, we propose an object-based video abstraction algorithm by cluster analysis using the mean shift algorithm. The generated clusters, called segments in this paper, can be used as a small unit in the object-based video indexing/retrieval systems. In the proposed algorithm, Hu's (1962) seven moments are used as shape descriptors for each video object plane (VOP), and shape distance between two VOPs is measured by using weighted Euclidean distance. Promising experimental results on the proposed scheme are presented.