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Abstract
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Article Information:
A Gray Texture Classification Using Wavelet and Curvelet Coefficients
M. Santhanalakshmi and K. Nirmala
Corresponding Author: M. Santhanalakshmi
Submitted: February 28,2014
Accepted: April 08, 2014
Published: June 25, 2014 |
Abstract:
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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.
Key words: Brodatz album, curvelet transform, nearest neighbor classifier, texture classification, wavelet transform, ,
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Cite this Reference:
M. Santhanalakshmi and K. Nirmala, . A Gray Texture Classification Using Wavelet and Curvelet Coefficients. Research Journal of Applied Sciences, Engineering and Technology, (24): 5258-5263.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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