Abstract
This study presents a framework for gray texture classification based on the fusion of wavelet and curvelet features. The two main frequency domain transformations Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT) are analyzed. The features are extracted from the DWT and DCT decomposed image separately and their performance is evaluated independently. Then feature fusion technique is applied to increase the classification accuracy of the proposed approach. Brodatz texture images are used for this study. The results show that, only two texture images D105 and D106 are misclassified by the fusion approach and 99.74% classification accuracy is obtained.
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