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Proceedings Paper

The role of classifiers in multimedia content management
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Paper Abstract

Enabling semantic detection and indexing is an important task in multimedia content management. Learning and classification techniques are increasingly relevant to the state of the art content management systems. From relevance feedback to semantic detection, there is a shift in the amount of supervision that precedes retrieval from light weight classifiers to heavy weight classifiers. In this paper we compare the performance of some popular classifiers for semantic video indexing. We mainly compare among other techniques, one technique for generative modeling and one for discriminant learning and show how they behave depending on the number of examples that the user is willing to provide to the system. We report results using the NIST TREC Video Corpus.

Paper Details

Date Published: 10 January 2003
PDF: 11 pages
Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); doi: 10.1117/12.479746
Show Author Affiliations
Milind Ramesh Naphade, IBM Thomas J. Watson Research Ctr. (United States)
John R. Smith, IBM Thomas J. Watson Research Ctr. (United States)

Published in SPIE Proceedings Vol. 5021:
Storage and Retrieval for Media Databases 2003
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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