TY - EJOU AU - Kumar, K. Naveen AU - Rao, K. Srinivasa AU - Srinivas, Y. AU - Satyanarayana, Ch. TI - Texture Segmentation based on Multivariate Generalized Gaussian Mixture Model T2 - Computer Modeling in Engineering \& Sciences PY - 2015 VL - 107 IS - 3 SN - 1526-1506 AB - Texture Analysis is one of the prime considerations for image analysis and processing. Texture segmentation gained lot of importance due to its ready applicability in automation of scene identification and computer vision. Several texture segmentation methods have been developed and analysed with the assumption that the feature vector associated with the texture of the image region is modelled as Gaussian mixture model. Due to the limitations of the Gaussian model being meso kurtic, it may not characterise the texture of all image regions accurately. Hence in this paper, a texture segmentation algorithm is developed and analysed with the assumption that the feature vector of the texture associated with the whole image is characterised by multivariate generalized Gaussian mixture model. The generalized Gaussian mixture model includes several lepto kurtic, platy kurtic and meso kurtic distributions as particular cases. The model parameters are estimated through EM algorithm. The segmentation algorithm is developed using maximum likelihood under Bayesian framework. The performance of the proposed algorithm is evaluated through segmentation quality metrics and conducting experimentation with a set of 8 sample images taken from Brodatz texture database. A comparative study of the proposed algorithm with that of Gaussian mixture model revealed that the proposed algorithm outstandthe existing algorithms. KW - Texture KW - Multivariate generalized gaussian mixture model KW - EM algorithm KW - Performance measures DO - 10.3970/cmes.2015.107.201