Abstract
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.
This work was supported by the EU project SCHEMA “Network of Excellence in Content-Based Semantic Scene Analysis and Information Retrieval” (IST-2001-32795).
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Mezaris, V., Strintzis, M.G. (2004). Object Segmentation and Ontologies for MPEG-2 Video Indexing and Retrieval. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds) Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27814-6_67
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DOI: https://doi.org/10.1007/978-3-540-27814-6_67
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