Texture classification via extended local graph structure

• This paper adopted a new version of Local Graph Structure Operator for texture classification.
• We have evaluated the operator on UIUC and XU high resolution texture databases.
• Another experiment is carried out to evaluate the robustness of the proposed operator against noise.

In this paper, we propose a simple and robust local descriptor operator, called the extended local graph structure (ELGS). The original local graph structure (LGS) performs very well in many domains, for instance face recognition, face spoofing detection and others. However, LGS has a few demerits such as LGS is not robust to the noise present in the image and LGS takes into considerations the horizontal graph and ignores the vertical graph which causes a loss in the spatial information. Therefore, we extend the idea of LGS by encoding the pattern into two directions. This is means that we take into consideration the vertical graph along with the horizontal graph and then concatenate the two computed histograms features to form a global descriptor. Experimental results on the UIUC and XU High Resolution texture databases show a promising performance.