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Texture classification using ridgelet transform
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DOI：10.1016/j.patrec.2006.04.013.png)
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Abstract
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Texture classification has long been an important research topic in image processing. Now a day's classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2-D is introduced. It allows representing edges and other singularities along lines in a more efficient way compared to wavelet transform. In this paper, the issue of texture classification based on ridgelet transform has been analyzed. Features are derived from the sub-bands of the ridgelet decomposition and are used for classification for the four different datasets containing 20, 30, 112 and 129 texture images respectively. Experimental results show that this approach allows obtaining high degree of success rate in classification. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
ridgelet transform
radon transform
texture classification
co-occurrence features
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