A New Similarity Measure for Random Signatures: Perceptually Modified Hausdorff Distance

Abstract

In most content-based image retrieval systems, the low level visual features such as color, texture and region play an important role. Variety of dissimilarity measures were introduced for an uniform quantization of visual features, or a histogram. However, a cluster-based representation, or a signature, has proven to be more compact and theoretically sound for the accuracy and robustness than a histogram. Despite of these advantages, so far, only a few dissimilarity measures have been proposed. In this paper, we present a novel dissimilarity measure for a random signature, Perceptually Modified Hausdorff Distance (PMHD), based on Hausdorff distance. In order to demonstrate the performance of the PMHD, we retrieve relevant images for some queries on real image database by using only color information. The precision vs. recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database.