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Proceedings Paper

New anchor selection methods for image retrieval
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Paper Abstract

Anchoring is a technique for representing objects by their distances to a few well chosen landmarks, or anchors. Objects are mapped to distance-based feature vectors, which can be used for content-based retrieval, classification, clustering, and relevance feedback of images, audio, and video. The anchoring transformation typically reduces dimensionality and replaces expensive similarity computations in the original domain with simple distance computations in the anchored feature domain, while guaranteeing lack of false dismissals. Anchoring is therefore surprisingly simple, yet effective, and flavors of it have seen application in speech recognition, audio classification, protein homology detection, and shape matching. In this paper, we describe the anchoring technique in some detail and study methods for anchor selection, both from an analytical, as well as empirical, standpoint. Most work to date has largely ignored this problem by fixing the anchors to be the entire set of objects or by using greedy selection from among the set of objects. We generalize previous work by considering anchors from outside of the object space, and by deriving an analytical upper bound on the distance-approximation error of the method.

Paper Details

Date Published: 10 January 2003
PDF: 8 pages
Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); doi: 10.1117/12.479744
Show Author Affiliations
Apostol Natsev, 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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