DocumentCode
476254
Title
Rotation invariant texture classification algorithm based on Curvelet transform and SVM
Author
Shang, Yan ; Diao, Yan-hua ; Li, Chun-Ming
Author_Institution
Dept. of Electron. & Inf., Hebei Univ. of Sci. & Technol., Shijiazhuang
Volume
5
fYear
2008
fDate
12-15 July 2008
Firstpage
3032
Lastpage
3036
Abstract
A rotation invariant texture classification algorithm based on curvelet transform and support vector machines (SVM) is proposed. The multidirectional and multiscale curvelet transform can offer more texture information and its energies are more compact as well as the SVM can achieve better classification. Compute the energies of the subbands acquired by curvelet transform to texture image first, then extract the rotation invariant feature vectors of isotropic, anisotropic and circular shift. The SVM algorithm is used to the texture classification at last. This method is compared with other rotation invariant texture classification algorithm, the experiment results show that it can improve the classification rate effectively.
Keywords
curvelet transforms; image classification; image texture; support vector machines; SVM; multiscale curvelet transform; rotation invariant texture classification algorithm; support vector machines; Classification algorithms; Cybernetics; Data mining; Discrete wavelet transforms; Feature extraction; Machine learning; Machine learning algorithms; Signal processing algorithms; Support vector machine classification; Support vector machines; Curvelet transform; Rotation invariance; Support vector machines; Texture classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location
Kunming
Print_ISBN
978-1-4244-2095-7
Electronic_ISBN
978-1-4244-2096-4
Type
conf
DOI
10.1109/ICMLC.2008.4620927
Filename
4620927
Link To Document