Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification

: 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

: 2019

IEEE Access

: IEEE ACCESS

: 7

: 110116

: 110127

: 12

: 2169-3536

DOI: https://doi.org/10.1109/ACCESS.2019.2932687

: 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.

Feature descriptor, Gaussian derivatives, local binary pattern, noise robust, texture classification, wavelet decomposition

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