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

Rotation invariant texture descriptors based on Gaussian Markov random fields for classification
Rotation invariant texture descriptors based on Gaussian Markov random fields for classification
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.
gaussian–markov random field, texture features, rotational invariance, texture classification
15-21
Dharmagunaw, C.
a2b31066-115f-43d2-9668-5a556504c852
Mahmoodi, S.
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, M.
e42f5213-4410-4284-9e96-74e359df6b19
Niranjan, M.
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Dharmagunaw, C.
a2b31066-115f-43d2-9668-5a556504c852
Mahmoodi, S.
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, M.
e42f5213-4410-4284-9e96-74e359df6b19
Niranjan, M.
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Dharmagunaw, C., Mahmoodi, S., Bennett, M. and Niranjan, M. (2016) Rotation invariant texture descriptors based on Gaussian Markov random fields for classification. Pattern Recognition Letters, 69, 15-21. (doi:10.1016/j.patrec.2015.10.006).

Record type: Article

Abstract

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.

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Accepted/In Press date: 5 October 2015
Published date: 22 October 2016
Keywords: gaussian–markov random field, texture features, rotational invariance, texture classification

Identifiers

Local EPrints ID: 382641
URI: http://eprints.soton.ac.uk/id/eprint/382641
PURE UUID: 38175b2d-21db-40c7-8d2e-f206fb40d844
ORCID for M. Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 08 Oct 2015 10:50
Last modified: 15 Mar 2024 03:29

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Contributors

Author: C. Dharmagunaw
Author: S. Mahmoodi
Author: M. Bennett
Author: M. Niranjan ORCID iD

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