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Visual Similarity Measurement with the Feature Contrast Model (2003)
| Citations: | 5 - 4 self |
BibTeX
@MISC{Eidenberger03visualsimilarity,
author = {Horst Eidenberger and Christian Breiteneder},
title = {Visual Similarity Measurement with the Feature Contrast Model},
year = {2003}
}
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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).
Keyphrases
feature contrast model visual similarity measurement similarity modeling euclidean distance second part first part worse performance metric-based distance measurement linear weighted merging query execution time non-subtractive configuration major drawback distance space new general similarity model questionable metric axiom general method fcm performs lr context distance measurement visual object similarity perception ideal distance measure used vir similarity model many vir system common property different parameterizations logical retrieval human peculiarity
