A stochastic framework for optimal key frame extraction from MPEG video databases

A framework for video content representation is proposed in this paper for extracting limited, but meaningful, information of video data directly from MPEG compressed domain. First, the traditional frame-based representation is transformed to a feature-based one. Then, all features are gathered together using a fuzzy formulation and extraction of several key frames is performed for each shot in a content-based rate sampling framework. In particular, our approach is based on minimization of a cross-correlation criterion among video frames of a given shot so as to be located a set of minimally correlated feature vectors. Experimental results indicating the good performance of the proposed scheme are also presented.