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Proceedings Paper

Visual similarity measurement with the feature contrast model
Author(s): Horst Eidenberger; Christian Breiteneder
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Paper Abstract

The focus of this paper is on similarity modeling. In the first part we revisit underlying concepts of similarity modeling and sketch the currently most used VIR similarity model (Linear Weighted Merging, LWM). Motivated by its drawbacks we introduce a new general similarity model called Logical Retrieval (LR) that offers more flexibility than LWM. In the second part we integrate the Feature Contrast Model (FCM) in this environment, developed by psychologists to explain human peculiarities in similarity perception. FCM is integrated as a general method for distance measurement. The results show that FCM performs (in the LR context) better than metric-based distance measurement. Euclidean distance is used for comparison because it is used in many VIR systems and is based on the questionable metric axioms. FCM minimizes the number of clusters in distance space. Therefore it is the ideal distance measure for LR. FCM allows a number of different parameterizations. The tests reveal that in average a symmetric, non-subtractive configuration that emphasizes common properties of visual objects performs best. Its major drawback in comparison to Euclidean distance is its worse performance (in terms of query execution time).

Paper Details

Date Published: 10 January 2003
PDF: 13 pages
Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003);
Show Author Affiliations
Horst Eidenberger, Technische Univ. Wien (Austria)
Christian Breiteneder, Technische Univ. Wien (Austria)

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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