A1 Refereed original research article in a scientific journal
Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification
Authors: Ayesha Saeed, Fawad, Muhammad Jamil Khan, Muhammad Ali Riaz, Humayun Shahid, Mansoor Shaukat Khan, Yasar Amin, Jonathan Loo, Hannu Tenhunen
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication year: 2019
Journal: IEEE Access
Journal acronym: IEEE ACCESS
Volume: 7
First page : 110116
Last page: 110127
Number of pages: 12
ISSN: 2169-3536
eISSN: 2169-3536
DOI: https://doi.org/10.1109/ACCESS.2019.2932687
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/42072252
A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper 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 get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The 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 paper with its parent descriptors and recently published paper is also presented.
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