Performance Analysis of DMD and SURF Methods for Texture Classification

The Texture is the crucial attribute in every image classification method as it describes the appearance of object. Today, different approaches of texture classification have been developed which focus on acquisition of image features from its texture and categorize them into different classes by using a particular classifier. This paper gives a state-of-the-art texture classification technique called Dense Micro- Block Difference (DMD). In this concept, image data representation is accomplished by capturing features in the form of micro-blocks. This method gives superior performance over already established methods in terms of processing time, accuracy and robustness and able to obtain whole image information. In this paper, we have taken UMD dataset for processing and calculated different performance parameters which gives excellent results than comparative method SURF.