Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification

Abstract

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.