Fuzzy framework for unsupervised video content characterization and shot classification

Abstract

In this paper we present a fuzzy framework for domaindependent analysis of video sequences. Fuzzy clustering and cluster validation methods are first employed to determine the number of distinct shot patterns and construct a reference model for a program or video domain of interest, using an appropriate training set. This model is subsequently utilized to assign new input data to the available classes by a fuzzy minimum-distance classifier. Additional domain-specific information can be introduced after classification to further enhance the annotations associated with every shot. The main advantage of the approach is that it builds a model for the input video automatically from training data, and thus eliminates the need for extensive user supervision. The fuzzy representation method improves the interpretability of the results, and reduces the number of erroneous classifications, since the continuous class affiliations of each input sample provide a confidence measure for the final assignments. The proposed approach presents a computationally efficient, unsupervised method for building browsable semantic descriptions of video sequences. Specifically, the algorithm can be used to generate various components of an MPEG-7-compliant description.