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Comparison of SMRT and Wavelet Texture Features

B. Manju, R. Gopikakumari

Abstract



Images can be characterized by their texture features. Texture features of images can be derived using statistical, transform based or structural methods. In this paper classification efficiency of texture features derived using two different transforms is compared. One of the texture feature sets is obtained using Sequency based Mapped Real Transform (SMRT). 16x16 SMRT is used to find the feature set comprising of 46 features. The feature set is optimized for number of features and classification accuracy using Genetic Algorithm. Tree structured wavelet transform is used to get the second feature set. Image is decomposed into sub image of size 16x16. Texture feature sets are derived using energy map of the sub image and is optimized. Experiment is performed on images from brodatz data base. Classification is performed using KNN classifier. Classification accuracy of SMRT and Wavelet texture features are compared.

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