Rotation invariant texture descriptors based on Gaussian Markov random fields for classification

Dharmagunaw, C., Mahmoodi, S., Bennett, M. and Niranjan, M. (2015) Rotation invariant texture descriptors based on Gaussian Markov random fields for classification Pattern Recognition Letters, 69, pp. 15-21. (doi:10.1016/j.patrec.2015.10.006).


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Local Parameter Histograms (LPH) based on Gaussian–Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are discussed namely, Rotation Invariant LPH (RI-LPH) and the Isotropic LPH (I-LPH) descriptors. Extensive texture classification experiments using traditional GMRF features, LPH features, RI-LPH and I-LPH features are performed. Furthermore comparisons to the current state-of-the-art texture features are made. Classification results demonstrate that LPH, RI-LPH and I-LPH features achieve significantly better accuracies compared to the traditional GMRF features. RI-LPH descriptors give the highest classification rates and offer the best texture discriminative competency. RI-LPH and I-LPH features maintain higher accuracies in rotation invariant texture classification providing successful rotational invariance.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.patrec.2015.10.006
Keywords: gaussian–markov random field, texture features, rotational invariance, texture classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ePrint ID: 382641
Date :
Date Event
5 October 2015Accepted/In Press
22 October 2016Published
Date Deposited: 08 Oct 2015 10:50
Last Modified: 17 Apr 2017 05:02
Further Information:Google Scholar

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