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Proceedings Paper

Validity-weighted model vector-based retrieval of video
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Paper Abstract

Model vector-based retrieval is a novel approach for video indexing that uses a semantic model vector signature that describes the detection of a fixed set of concepts across a lexicon. The model vector basis is created using a set of independent binary classifiers that correspond to the semantic concepts. The model vectors are created by applying the binary detectors to video content and measuring the confidence of detection. Once the model vectors are extracted, simple techniques can be used for searching to find similar matches in a video database. However, since confidence scores alone do not capture information about the reliability of the underlying detectors, techniques are needed to ensure good performance in the presence of varying qualities of detectors. In this paper, we examine the model vector-based retrieval framework for video and propose methods using detector validity to improve matching performance. In particular, we develop a model vector distance metric that weighs the dimensions using detector validity scores. In this paper, we explore the new model vector-based retrieval method for video indexing and empirically evaluate the retrieval effectiveness on a large video test collection using different methods of measuring and incorporating detector validity indicators.

Paper Details

Date Published: 18 December 2003
PDF: 9 pages
Proc. SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia 2004, (18 December 2003); doi: 10.1117/12.536579
Show Author Affiliations
John R. Smith, IBM Thomas J. Watson Research Ctr. (United States)
Ching-Yung Lin, IBM Thomas J. Watson Research Ctr. (United States)
Milind Ramesh Naphade, IBM Thomas J. Watson Research Ctr. (United States)
Apostol Natsev, IBM Thomas J. Watson Research Ctr. (United States)
Belle L. Tseng, IBM Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 5307:
Storage and Retrieval Methods and Applications for Multimedia 2004
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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