Saeed, Ayesha, Fawad, Fawad, Khan, Muhammad Jamil, Riaz, Muhammad Ali, Shahid, Humayun, Khan, Mansoor Shaukat, Amin, Yasar, Loo, Jonathan ORCID: https://orcid.org/0000-0002-2197-8126 and Tenhunen, Hannu (2019) Robustness-driven hybrid descriptor for noise-deterrent texture classification. IEEE Access, 7. pp. 110116-110127.

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Abstract

A robustness-driven hybrid descriptor for noise-deterrent texture classification is presented in this paper. This work offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to obtain a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analysed using renowned datasets: Outex original, Outex extended and KTH-TIPS. Experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52% and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed work with its parent descriptors and recently published work is also demonstrated.

Item Type:	Article
Identifier:	10.1109/ACCESS.2019.2932687
Additional Information:	(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.
Subjects:	Computing > Intelligent systems
Depositing User:	Jonathan Loo
Date Deposited:	29 Jul 2019 13:39
Last Modified:	04 Nov 2024 11:52
URI:	https://repository.uwl.ac.uk/id/eprint/6299

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