Performance Evaluation of Different Local Binary Operators for Texture Classification

Shamaileh, Abeer and Rassem, Taha H. and Liew, Siau-Chuin and Al Sayaydeh, Osama Nayel (2019) Performance Evaluation of Different Local Binary Operators for Texture Classification. International Journal of Advanced Computer Science and Applications (IJACSA). ISSN 2156-5570 (Online). (In Press / Online First) (In Press / Online First)

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Abstract

Local Binary Patterns (LBP) have brightened up as one of the most eminent and widely studied texture descriptors. The LBP descriptor has gained high acceptance due to its simplicity, high distinguishing power, and flexibility. As such, it has been deployed in several applications where it has performed well. This is why LBP is the basis for a new research direction. Several forms of LBP have been suggested to increase its discriminative ability during texture classification and to improve its robustness to noise. Since 2002, different texture descriptors had been proposed. These texture descriptors were inspired by LBP and proposed to overcome its limitations. Examples of these texture descriptors are Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC), Completed Local Ternary Pattern (CLTP), and Wavelet Completed Local Ternary Pattern (WCLTP). Due to the importance of texture descriptors in image classification, the performance of different texture descriptors is studied and investigated for image texture classification in this paper. This study also strived to improve the role of image texture information in classification processes. Different experiments were conducted using two benchmark texture datasets - CuRTex and OuTex. The experimental results showed that the WCLTP outperformed the remaining texture descriptors. The WCLTP achieved 99.35% and 96.89% classification performance accuracy with CuRTex and OuTRex, respectively.

Item Type:	Article
Uncontrolled Keywords:	LBP; Texture classification; Texture descriptor; OuTex dataset; CuRTex dataset
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Dr. Taha Hussein Alaaldeen Rassem
Date Deposited:	29 May 2019 04:49
Last Modified:	29 May 2019 04:49
URI:	http://umpir.ump.edu.my/id/eprint/24773
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• Performance Evaluation of Different Local Binary Operators for Texture Classification. (deposited 29 May 2019 04:49) [Currently Displayed]
  • Different local binary operators for texture classification: a comparative study. (deposited 20 Nov 2019 08:08)

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