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

• Rotation invariant descriptors based on Local Parameter Histograms are proposed.
• Two approaches are suggested which produce RI-LPH and I-LPH descriptors.
• RI-LPH descriptor is formulated by circular shifting the neighbour values.
• I-LPH descriptor is generated using isotropic Gaussian–Markov Random Fields.
• Both descriptors achieve higher classification accuracies in invariant texture analysis.

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.