ATTENTION: This is a web archive! The IMS Group was split up in 2018 and does not exist anymore. Recent work of former members can be found at the VR/AR Group and the Computer Vision Group.

Interactive Media Systems, TU Wien

Visual Similarity Measurement with the Feature Contrast Model

By Horst Eidenberger and Christian Breiteneder

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).

Reference

H. Eidenberger, C. Breiteneder: "Visual Similarity Measurement with the Feature Contrast Model"; in: "SPIE Electronic Imaging Conference", SPIE, 2003, ISBN: 0819448214.

BibTeX

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