Faward, Faward, Khan, Muhammad Jamil, Riaz, Muhammad Ali, Shahid, Humayun, Khan, Mansoor Shaukat, Loo, Jonathan ORCID: https://orcid.org/0000-0002-2197-8126 and Tenhunen, Hannu (2019) Texture representation through overlapped multi-oriented tri-scale local binary pattern. IEEE Access, 7. pp. 66668-66679.

Preview

PDF Loo_etal_IEEE_Access_2019_Texture_representation_through_overlapped_multi-oriented_tri-scale_local_binary_pattern.pdf - Accepted Version Download (14MB) | Preview

Abstract

This paper ideates a novel texture descriptor that retains its classification accuracy under varying conditions of image orientation, scale, and illumination. The proposed Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP) texture descriptor also remains insensitive to additive white Gaussian noise. The wavelet decomposition stage of the OMTLBP provides robustness to photometric variations, while the two subsequent stages – overlapped multi-oriented fusion and multi-scale fusion – provide resilience against geometric transformations within an image. Isolated encoding of constituent pixels along each scale in the joint histogram enables the proposed descriptor to capture both micro and macro structures within the texture. Performance of the OMTLBP is evaluated by classifying a variety of textured images belonging to Outex, KTH-TIPS, Brodatz, CUReT, and UIUC datasets. The experimental results validate the superiority of the proposed method in terms of classification accuracy when compared with the state-of-the-art texture descriptors for noisy images.

Item Type:	Article
Identifier:	10.1109/ACCESS.2019.2918004
Additional Information:	This work was supported in part by the Higher Education Commission (HEC) of Pakistan under Technology Development Fund underGrant TDF-67/2017, and in part by the ASR&TD-UETT Faculty Research Grant. (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Keywords:	Classification, geometric transformations, photometric variations, texture representation, wavelet decomposition
Subjects:	Computing > Intelligent systems Computing > Systems
Depositing User:	Jonathan Loo
Date Deposited:	13 Jun 2019 10:55
Last Modified:	04 Nov 2024 11:53
URI:	https://repository.uwl.ac.uk/id/eprint/6136

Downloads

Actions (login required)

View Item