Completed Local Ternary Pattern for Rotation Invariant Texture Classification

Rassem, Taha H. and Bee, Ee Khoo (2014) Completed Local Ternary Pattern for Rotation Invariant Texture Classification. Scientific World Journal, 2014 (373254). pp. 1-10. ISSN 2356-6140

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Abstract

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

Item Type:	Article
Subjects:	T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Divisions:	Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Article
Depositing User:	Mr Noorazilan Noordin
Date Deposited:	23 Jan 2018 03:55
Last Modified:	23 Jan 2018 03:55
URI:	http://eprints.usm.my/id/eprint/38476

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