Title :
Texture Image Classification Using Non-subsampled Contourlet Transform and Support Vector Machines
Author :
Li, Yi ; Liu, Guanzhong
Author_Institution :
Sch. of Bus. & Sch. of Art, Central South Univ., Changsha, China
Abstract :
This paper proposes a new approach to characterize texture image at multiresolution using the non-subsampled contourlet transform, a new geometrical multiresolution transform. The support vector machines (SVMs), which have demonstrated excellent performance in a variety of pattern recognition problems, are used as classifiers. The Classification experiments with 20 Brodatz textures indicate that the NSCT and SVM approach is superior to standard wavelet transform method.
Keywords :
image texture; pattern recognition; support vector machines; wavelet transforms; Brodatz textures; NSCT; SVM approach; non subsampled contourlet transform; pattern recognition problems; standard wavelet transform method; support vector machines; texture image classification; texture image multiresolution; Art; Electronic mail; Feature extraction; Filter bank; Image classification; Image resolution; Low pass filters; Support vector machine classification; Support vector machines; Wavelet transforms; non-subsampled contourlet transform; support vector machines; texture classification; wavelet transform;
Conference_Titel :
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on
Conference_Location :
Changsha
Print_ISBN :
978-0-7695-3865-5
DOI :
10.1109/ISCID.2009.88
