Abstract

In this work we describe a novel statistical video representa- tion and modeling scheme. Video representation schemes are needed to enable segmenting a video stream into meaningful video-ob jects, useful for later indexing and retrieval applications. In the proposed method- ology, unsupervised clustering via Guassian mixture modeling extracts coherent space-time regions in feature space, and corresponding coher- ent segments (video-regions) in the video content. A key feature of the system is the analysis of video input as a single entity as opposed to a sequence of separate frames. Space and time are treated uniformly. The extracted space-time regions allow for the detection and recognition of video events. Results of segmenting video content into static vs. dynamic video regions and video content editing are presented.