Open Access

ARTICLE

Texture Segmentation based on Multivariate Generalized Gaussian Mixture Model

by K. Naveen Kumar¹, K. Srinivasa Rao², Y. Srinivas³, Ch. Satyanarayana⁴

1 Dept of IT, GIT, GITAM University, Visakhapatnam, A.P, INDIA, Email: nkumarkuppili@gmail.com, naveen_it@gitam.edu
2 Dept of Statistics, Andhra University, Visakhapatanam, A.P, INDIA, Email: ksraoau@yahoo.co.in
3 Dept of IT, GIT, GITAM University, Visakhapatnam, A.P, INDIA, Email: drysr@gitam.edu
4 Dept of CSE, JNTUK-Kakinada, Kakinada, A.P, INDIA, Email: chsatyanarayana@yahoo.com

Computer Modeling in Engineering & Sciences 2015, 107(3), 201-221. https://doi.org/10.3970/cmes.2015.107.201

Abstract

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.

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Keywords

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