FSMIQ: fuzzy similarity matching for image queries

In this paper we present FSMIQ, a novel image retrieval system that is based on fuzzy similarity metrics. FSMIQ uses shape and color information to generate a 2630 byte long information vector that describes the shape and color distribution in the image. This information is generated by the application of the discrete wavelet transform to the YIQ color space and picking the appropriate information by quantization of the Y channel coefficients and using fuzzy linguistic variables for color description. The information vectors that correspond to the images in a given database are used for the queries. Queries are carried out using the generalized Tversky index, a similarity index that is based on human similarity perception, which has been developed by the authors. Different retrieval results are calculated for shape and color; a final data fusion process takes place to provide the overall results. For examining the efficiency of FSMIQ, we use the AVRR and IAVRR metrics proposed by Flickner et al. (1995). Our experiments indicate very good performance, both visually and based on the aforementioned metrics