A Review on Image Texture Analysis Methods

Texture classification is an active topic in image processing which plays an important role in many applications such as image retrieval, inspection systems, face recognition, medical image processing, etc. There are many approaches extracting texture features in gray-level images such as local binary patterns, gray level co-occurence matrixes, statistical features, skeleton, scale invariant feature transform, etc. The texture analysis methods canbe categorized in 4 groups titles: statistical methods, structural methods, filter-based and modelbased approaches. In many related researches, authors have tried to extract color and texture features jointly. In this respect, combinated methods are considered as efficient image analysis descriptors. Mostly important challenages in image texture analysis are rotation sensitivity, gray scale variations, noise sensitivity, illumination and brightness conditions, etc. In this paper, we review most efficient and state-of-theart image texture analysis methods. Also, some texture classification approaches are survived.