Texture classification using Curvelet Statistical and Co-occurrence Features

Texture classification has long been an important research topic in image processing. Now a days 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. Recentl...

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Bibliographic Details
Main Authors: Arivazhagan, S., Ganesan, L., Kumar, T.G.S.
Format: Conference Proceeding
Language:English
Subjects:
Autocorrelation
Biomedical signal processing
Educational institutions
Filter bank
Filtering
Gabor filters
Image analysis
Image processing
Image texture analysis
Wavelet transforms
Online Access:Request full text
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Summary:Texture classification has long been an important research topic in image processing. Now a days 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 2D is introduced. But images often contain curves rather than straight lines, so curvelet transform is designed to handle it. It allows representing edges and other singularities along lines in a more efficient way when compared with other transforms. In this paper, the issue of texture classification based on curvelet transform has been analyzed. Curvelet statistical features (CSFs) and curvelet co-occurrence features (CCFs) are derived from the sub-bands of the curvelet decomposition and are used for classification. Experimental results show that this approach allows obtaining high degree of success rate in classification
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2006.1110