RECENT TRENDS IN TEXTURE CLASSIFICATION: A REVIEW

Abstract

Texture classification is used in various pattern recognition applications that possess feature-liked appearance. This paper aims to compile the recent trends on the usage of feature extraction and classification methods used in the research of texture classification as well as the texture datasets used for the experiments. The study shows that the signal processing methods, such as Gabor filters and wavelets are gaining popularity but old methods such as GLCM are still used but are improved with new calculations or combined with other methods. For the classifiers, nearest neighbor algorithms are still fairly popular despite being simple and SVM has become a major classifier used in texture classification. For the datasets, Brodatz texture dataset is the most popularly used dataset despite it being old and with limited samples, other datasets are less used.