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Survey On Image Texture Classification Techniques

Vishal S.Thakare , Nitin N. Patil and Jayshri S. Sonawane

Recent advances in digital imaging technology, computational speed, storage capacity and networking have made it possible to capture, manipulate, store, and transmit images at interactive speeds with equipment available at every home or business. As a result, images have become a dominant part of information exchange. They are used for entertainment, education, commerce, medicine, science, and other applications. The rapid accumulation of large collections of digital images has created the need for efficient and intelligent schemes for image classification. Texture is an important feature of objects in an image .Nowadays there has been a great interest in the development of texture based Image Classification methods in many different areas. Most of the image texture classification systems use the gray-level co-occurrence matrices (GLCM) and selforganizing map (SOM) methods. The GLCM is a matrix of how often different combinations of pixel brightness values (grey levels) occur in an image. The GLCM matrices extracted from an image database are processed to create the training data set for a SOM neural network. The SOM model organizes and extracts prototypes from processed GLCM matrices.