  Tactile texture refers to the tangible feel of a surface and visual texture
refers to see the shape or contents of the image. In the image processing, the
texture can be defined as a function of spatial variation of the brightness
intensity of the pixels. Texture is the main term used to define objects or
concepts of a given image. Texture analysis plays an important role in computer
vision cases such as object recognition, surface defect detection, pattern
recognition, medical image analysis, etc. Since now many approaches have been
proposed to describe texture images accurately. Texture analysis methods
usually are classified into four categories: statistical methods, structural,
model-based and transform-based methods. This paper discusses the various
methods used for texture or analysis in details. New researches shows the power
of combinational methods for texture analysis, which can't be in specific
category. This paper provides a review on well known combinational methods in a
specific section with details. This paper counts advantages and disadvantages
of well-known texture image descriptors in the result part. Main focus in all
of the survived methods is on discrimination performance, computational
complexity and resistance to challenges such as noise, rotation, etc. A brief
review is also made on the common classifiers used for texture image
classification. Also, a survey on texture image benchmark datasets is included.

Computer Vision and Pattern Recognition