Table of Content

Open Access

ARTICLE

Texture Segmentation based on Multivariate Generalized Gaussian Mixture Model

K. Naveen Kumar1, K. Srinivasa Rao2, Y. Srinivas3, Ch. Satyanarayana4
Dept of IT, GIT, GITAM University, Visakhapatnam, A.P, INDIA, Email: nkumarkuppili@gmail.com, naveen_it@gitam.edu
Dept of Statistics, Andhra University, Visakhapatanam, A.P, INDIA, Email: ksraoau@yahoo.co.in
Dept of IT, GIT, GITAM University, Visakhapatnam, A.P, INDIA, Email: drysr@gitam.edu
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.

Keywords

Texture, Multivariate generalized gaussian mixture model, EM algorithm, Performance measures.

Cite This Article

Kumar, K. N., Rao, K. S., Srinivas, Y., Satyanarayana, C. (2015). Texture Segmentation based on Multivariate Generalized Gaussian Mixture Model. CMES-Computer Modeling in Engineering & Sciences, 107(3), 201–221.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 742

    View

  • 502

    Download

  • 0

    Like

Share Link

WeChat scan