Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification

• Published in: IEEE access Vol. 7; pp. 110116 - 110127
• Main Authors: Saeed, Ayesha, Fawad, Jamil Khan, Muhammad, Riaz, Muhammad Ali, Shahid, Humayun, Khan, Mansoor Shaukat, Amin, Yasar, Loo, Jonathan, Tenhunen, Hannu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AWGN; Classification; Datasets; Feature descriptor; Feature extraction; Gaussian derivatives; Image acquisition; Lighting; local binary pattern; Noise; Noise measurement; noise robust; Noise robustness; Robustness; Texture; texture classification; wavelet decomposition; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2932687

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.