Similarity Measure and Learning with Gray Level Aura Matrices (GLAM) for Texture Image Retrieval

Abstract

We present a new similarity measure for texture images based on the gray level aura matrices (GLAM), originally proposed by Elfadel and Picard for modeling textures. With the new similarity measure, a support vector machine (SVM) is used to learn pattern similarities for texture image retrieval. In our approach, a texture image is first segmented into clusters of gray level sets. Defined based on the aura measures, a normalized aura matrix is calculated between the gray level sets of the image. The similarity between two texture images computed by the distance of their corresponding normalized aura matrices is defined as the aura matrix distance. The smaller the distance, the more similar are the two textures. To enable the learning of similarity for texture image retrieval, an existing SVM method is adapted to our application, but with a different similarity measure function, different texture feature vectors, and a different similarity ranking scheme for the final retrieved images based on the GLAM. We compare our approach experimentally with existing approaches by performing texture image retrieval from the Brodatz database and the Vistex database. The experimental results show that the proposed approach has performance significantly better than existing approaches with an average successful retrieval rate of 99% - 100% vs 89% - 92% using other approaches.