HEAT: HIERARCHICAL ENTROPY APPROACH FOR TEXTURE INDEXING IN IMAGE DATABASES

Abstract

This paper illustrates a method, called HEAT, for image indexing based on texture information. The texture's partitioning element is first put into 1-D form and then its Hierarchical Entropy-based Representation is obtained. This representation is used to index the texture in the space of features. The same representation is well suited for contour data, and it has invariance and robustness properties that make it attractive for incorporation into larger systems. A comparison with another performing method is carried out, and the experiments show that the two techniques have slightly different strong points, suggesting different fields of application. In the experimental section, a case study involving over 2500 mammographies from different sources is presented and discussed.