A Gray Texture Classification Using Wavelet and Curvelet Coefficients
نویسنده
چکیده
This study presents a framework for gray texture classification based on 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 performances are evaluated independently. The performance metric used to analyze the system is classification accuracy. The standard benchmark database, Brodatz texture images are used for this study. The results show that, the curvelet based features provides better accuracy than wavelet based features.
منابع مشابه
Texture classification and discrimination for region-based image retrieval
In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, sufficient information needs to be extracted from their sub-bands for effective texture classification....
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