Local Binary Pattern (LBP) and its variants have considerable success in a wide range of computer vision and pattern recognition applications, especially in tasks related to texture classification. However, the LBP method is sensitive to noise, scale variations and unable to capture macro-structure information. We propose a novel texture classification descriptor called Scale Adaptive Robust LBP (SARLBP) that enhances macro-level descriptive information by incorporating significantly larger scales, and a novel encoding scheme, which is designed to overcome the limitations of traditional LBP schemes. SARLBP method dynamically determines a single optimal scale for each radial direction from multiple scales based on the local area's characteristics. Subsequently, this descriptor extracts four distinct patterns derived from regional image medians of center pixel, radially-optimized neighbor pixels, optimized fixed scale-based pixels, and radial-difference-based pixels. This method adeptly captures texture information at both micro and macro scales by employing scale adaptation based on the distinctive attributes of the local region. As a result, it provides a comprehensive and robust representation of the texture images. Extensive experimentation was conducted on four publicly available texture databases (ALOT, CUReT, UMD, and Kylberg), considering both the presence and absence of two distinct types of interference (Gaussian noise and Salt-and-Pepper noise). The results reveal that our SARLBP method achieves significantly better performance than other state-of-the-art LPB variants with a fixed smaller feature dimension.

Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined$n$number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the$n$features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.