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Proceedings Paper

S-STIR: similarity search through iterative refinement
Author(s): Chung-Sheng Li; John R. Smith; Vittorio Castelli
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Paper Abstract

Similarity retrieval of images based on texture and color features has generated a lot of interests recently. Most of these similarity retrievals are based on the computation of the Euclidean distance between the target feature vector and the feature vectors in the database. Euclidean distance, however, does not necessarily reflect either relative similarity required by the user. In this paper, a method based on nonlinear multidimensional scaling is proposed to provide a mechanism for the user to dynamically adjust the similarity measure. The results show that a significant improvement on the precision versus recall curve has been achieved.

Paper Details

Date Published: 23 December 1997
PDF: 9 pages
Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298458
Show Author Affiliations
Chung-Sheng Li, IBM Thomas J. Watson Research Ctr. (United States)
John R. Smith, IBM Thomas J. Watson Research Ctr. (United States)
Vittorio Castelli, IBM Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 3312:
Storage and Retrieval for Image and Video Databases VI
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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