Abstract
In image processing, regular repetition of an element is known as texture. Texture classification is a process of assigning an unknown texture to a known set of texture class. The real applications of texture classification are remote sensing, medical imaging, industrial inspection and pattern recognition. In most of the developed texture classification, the resulting classification accuracy is highly affected by random noise. And also most of the presented approaches use either local texture features or both local and global features. Extracting texture features that is rotation-invariant, insensitive to noise and classification accuracy is still a challenge. This survey comprised with a brief overview of the most common classification techniques, and a comparison between them. It discusses the various feature extraction methods.