Texture classification via extended local graph structure

Citation

Bashier, Housam Khalifa and Lau, Siong Hoe and Liew, Tze Hui and Abdullah, Mohd Fikri Azli and Pang, Ying Han and Wee, Kouk Kwee and Sayeed, Md. Shohel (2016) Texture classification via extended local graph structure. Optik - International Journal for Light and Electron Optics, 127 (2). pp. 638-643. ISSN 0030-4026

[img] Text
144.pdf
Restricted to Repository staff only

Download (1MB)

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Local binary pattern, Texture classification, Local graph structure
Subjects: Q Science > QA Mathematics > QA150-272.5 Algebra
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 06 Jul 2020 05:11
Last Modified: 06 Jul 2020 05:11
URII: http://shdl.mmu.edu.my/id/eprint/6738

Downloads

Downloads per month over past year

View ItemEdit (login required)