An Automatic Video Classification System Based on a Combination of HMM and Video Summarization

Abstract

As digital libraries and video databases grow, we need methods to assist us in the synthesis and analysis of digital video. Since the information in video databases can be measured in thousands of gigabytes of uncompressed data, tools for efficient summarizing and indexing of video sequences are indispensable. In this paper, we present a method for effective classification of different types of videos that makes use of a video summarization that is in the form of a storyboard of keyframes. To produce the summarization, we first generate a universal basis on which to project a video frame that effectively reduces any video to the same lighting conditions. Each frame is represented by a compressed chromaticity signature. We then set out a multi-stage hierarchical clustering method to efficiently summarize a video. Finally we classify TV videos using a trained Hidden Markov Model that utilizes the compressed chromaticity signatures as well as temporal features of videos derived from their keyframe summaries.