Gaussian Markov random field based improved texture descriptor for image segmentation

Dharmagunawardhana, Chathurika, Mahmoodi, Sasan, Bennett, Michael and Niranjan, Mahesan (2014) Gaussian Markov random field based improved texture descriptor for image segmentation Image and Vision Computing, 32, (11), pp. 884-895. (doi:10.1016/j.imavis.2014.07.002).


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This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields (GMRFs). A spatially localized parameter estimation technique using local linear regression is performed and the distributions of local parameter estimates are constructed to formulate the texture features. The inconsistencies arising in localized
parameter estimation are addressed by applying generalized inverse, regularization and an estimation window size selection criterion. The texture descriptors are named as local parameter histograms (LPHs) and are used in texture segmentation with the k-means clustering algorithm. The segmentation results on general texture datasets demonstrate that LPH descriptors significantly improve the performance of classical GMRF features and achieve better results compared to the state-of-the-art texture descriptors based on local feature distributions. Impressive natural image segmentation results are also achieved and comparisons to the other standard natural image segmentation algorithms are also presented. LPH descriptors produce promising texture features that integrate both statistical and structural information about a texture. The region boundary localization can be further improved by integrating colour information and using advanced segmentation algorithms.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.imavis.2014.07.002
ISSNs: 0262-8856 (print)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Organisations: Southampton Wireless Group
ePrint ID: 367528
Date :
Date Event
21 July 2014Accepted/In Press
27 July 2014e-pub ahead of print
November 2014Published
Date Deposited: 31 Jul 2014 12:04
Last Modified: 20 Jun 2017 16:35
Further Information:Google Scholar

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