Model-based semantic object extraction for content-based video representation and indexing

This paper proposes an integrated system for supporting content-based video retrieval and browsing over networks. An automatic semantic video object extraction technique for providing more compact video representation is developed. The video images are first partitioned into a set of homogeneous regions with accurate boundaries by integrating the results of color edge detection and region growing procedures. The object seeds, which are the intuitive and representative part of the semantic objects, are detected from these obtained homogeneous image regions. The semantic objects are then generated by a seeded region aggregation or a human interaction procedure. These obtained semantic objects are tracked along the time axis for exploiting their temporal correspondences among frames. Given the semantic video objects represented by a set of visual features, a seeded semantic video content clustering technique is developed for providing more effective video indexing, retrieval and browsing.