texture classification is an active topic in image processing. There are many approaches to extracting texture features in gray-level images. The texture analysis methods can be categorized in 4 groups.
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-occurrence matrices, statistical features,
skeleton, scale invariant feature transform, etc. The texture analysis methods
can be categorized in 4 groups titles: statistical methods, structural methods,
filter-based and model based approaches. In many related researches, authors
have tried to extract color and texture features jointly. In this respect,
combined methods are considered as efficient image analysis descriptors. Mostly
important challenges 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-the-art image texture
analysis methods. Also, some texture classification approaches are survived.