Video Summarization: A Machine Learning Based Approach

In this paper we propose schemes for using learning in video analysis tasks like content based filtering and shot summarization. Shot segmentation is performed by our neuro-fuzzy framework, which extracts fuzzy rules for video segmentation from the trained neuro-fuzzy network. We explore Independent Component Analysis and extract Independent Components that act as features for describing the content of a shot. We prove our claim by showing simple results of a Content Based Filtering scheme based on this. We also propose a technique for summarizing content of a video shot. Unlike, keyframe based approaches we try to find out those ”critical windows” from the shot sequence that best describes the content of a shot. Hierchical clustering of these windows provide the summarization of the shots. This scheme thus preserves the original component objects that make up the video thus characterizing the semantically essential information present in the video.