GRADUAL TRANSITION DETECTION USING COLOR COHERENCE AND OTHER CRITERIA IN A VIDEO SHOT META-SEGMENTATION FRAMEWORK

Shot segmentation provides the basis for almost all high-level video content analysis approaches, validating it as one of the major prerequisites for efficient video semantic analysis, indexing and retrieval. The successful detection of both gradual and abrupt transitions is necessary to this end. In this paper a new gradual transition detection algorithm is proposed, that is based on novel criteria such as color coherence change that exhibit less sensitivity to local or global motion than previously proposed ones. These criteria, each of which could serve as a standalone gradual transition detection approach, are then combined using a machine learning technique, to result in a meta-segmentation scheme. Besides significantly improved performance, advantage of the proposed scheme is that there is no need for threshold selection, as opposed to what would be the case if any of the proposed features were used by themselves and as is typically the case in the relevant literature. Performance evaluation and comparison with four other popular algorithms reveals the effectiveness of the proposed technique.