Motion Pattern-Based Video Classification and Retrieval

Abstract

Today′s content-based video retrieval technologies are still far from human′s requirements. A fundamental reason is the lack of content representation that is able to bridge the gap between visual features and semantic conception in video. In this paper, we propose a motion pattern descriptor, motion texture that characterizes motion in a generic way. With this representation, we design a semantic classification scheme to effectively map video clips to semantic categories. Support vector machines (SVMs) are used as the classifiers. In addition, this scheme also improves significantly the performance of motion-based shot retrieval due to the comprehensiveness and effectiveness of motion pattern descriptor and the semantic classification capability as shown by experimental evaluations.