Vasileios Mezaris1 and Michael G. Strintzis,
Object Segmentation and Ontologies for MPEG-2 Video
Indexing and Retrieval,
In Proceedings of the Third International Conference on Image
and Video Retrieval (CIVR 2004),
Dublin, Ireland, No. 3115 in Lecture Notes in Computer Science, pp. 573-581, Springer-Verlag, July 21-23 2004.A novel approach to object-based video indexing and retrieval is presented, employing an object segmentation algorithm for the real-time, unsupervised segmentation of compressed image sequences and simple ontologies for retrieval. The segmentation algorithm uses motion information directly extracted from the MPEG-2 compressed stream to create meaningful foreground spatiotemporal objects, while background segmentation is additionally performed using color information. For the resulting objects, MPEG-7 compliant low-level indexing descriptors are extracted and are automatically mapped to appropriate intermediate-level descriptors forming a simple vocabulary termed object ontology. This, combined with a relevance feedback mechanism, allows the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) and the retrieval of relevant video segments. Experimental results demonstrate the effectiveness of the proposed approach.