Asymmetric power distribution model of wavelet subbands for texture classification

The generalized Gaussian distribution (GGD) is a well established statistical model for wavelet subband characterization used in several applications. However, it is not really suitable for eventual asymmetry of probability density functions. Therefore, in this paper we propose to exploit the asymmetric power distribution (APD) which is a more general and flexible model than the GGD. The APD parameters are estimated through the maximum-likelihood estimation. A supervised texture classification problem is proposed as an application in this work. It is based on the Bayesian framework which has led to the definition of the closed form of the corresponding Kullback–Leibler divergence considered as a similarity measure. To validate the APD model, the goodness-of-fit using the classical Kolmogorov–Smirnov test is used. Finally, classification results on four databases demonstrate the interest of the proposed approach.