Similarity measures for image databases

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Abstract
Similarity between images is used for storage and retrieval in image databases. In the literature, several similarity measures have been proposed that may be broadly categorized as: (1) metric based, (2) set-theoretic based, and (3) decision-theoretic based measures. In each category, measures based on crisp logic as well as fuzzy logic are available, In some applications such as image databases, measures based on fuzzy logic would appear to be naturally better suited, although so far no comprehensive experimental study has been undertaken. In this paper, we report results of some of the experiments designed to compare various similarity measures for application to image databases. We are currently working with texture images and intend to work with face images in the near future. As a first step for comparison, the similarity matrices for each of the similarity measure is computed over a set of selected textures and are presented as visual images. Comparative analysis of these images reveals the relative characteristics of each of these measures. Further experiments are needed to study their sensitivity to small changes in images such as illumination, magnification, orientation etc. We describe these experiments (sensitivity analysis, transition analysis etc.) that are currently in progress.

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