Supervised Texture Classification Using Characteristic Generalized Gaussian Density

Abstract

Generalized Gaussian density (GGD) is a well established model for high frequency wavelet subbands and has been applied in texture image retrieval with very good results. In this paper, we propose to adopt the GGD model in a supervised learning context for texture classification. Given a training set of GGDs, we define a characteristic GGD (CGGD) that minimizes its Kullback-Leibler distance (KLD) to the training set. We present mathematical analysis that proves the existence of our characteristic GGD and provide a sufficient condition for the uniqueness of CGGD, thus establishing a theoretical basis for its use. Our experimental results show that the proposed CGGD signature together with the use of KLD has a very promising recognition performance compared with existing approaches.