Fuzzy hamming distance in a content-based image retrieval system

Abstract

The performance of content-based image retrieval (CBIR) systems mainly depends on the image similarity measure that it uses. The fuzzy Hamming distance (D) is an extension of the Hamming distance for real-valued vectors. Because the feature space of each image is real-valued, the fuzzy Hamming distance can be successfully used as an image similarity measure. The current study reports on the results of applying D as a similarity measure between the color histograms of two images. The fuzzy Hamming distance is suitable for this application because it can take into account not only the number of different colors but also the magnitude of this difference.