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Proceedings Paper

Similiarity distances evaluation for query by example retrieval
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Paper Abstract

Query by example is a common model developed for content-based image retrieval. The purpose of such a tool is to extract from a large database the most similar images to a request one. In practice, the meaningful characteristics of each image are first extracted. Then, each region is described with a vector composed with classical statistical features or spatial relationships. Finally, the system proposes to the user the images that minimize a certain similarity distance computed on each vector. Nevertheless, query by example depends on a criterion determined by the user. Objectively, this last step of any content-based retrieval system then suffers from a large difficulty to express the real hope of the user. Thus, the results are always constrained to the similarity distance definition. In actual fact, it is not sufficient to compute good descriptors, a robust and adequate distance to compare them is also necessary. Our purpose is more precisely to evaluate different similarity "blob-to-blob" distances. In fact, each image is first described locally using a coarse segmentation and the meaningful regions are extracted using a selection process based on color homogeneity. Among all these parameters, different distances are discussed using different approaches: spatial, shape, color and texture similarities.

Paper Details

Date Published: 10 January 2003
PDF: 12 pages
Proc. SPIE 5018, Internet Imaging IV, (10 January 2003); doi: 10.1117/12.476187
Show Author Affiliations
Jerome Da Rugna, Univ. Jean Monnet (France)
Hubert Konik, Univ. Jean Monnet (France)

Published in SPIE Proceedings Vol. 5018:
Internet Imaging IV
Simone Santini; Raimondo Schettini, Editor(s)

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