Texture classification using combined image decomposition methods

Authors:
Bin Yang;Shutao Li
Affiliations:
College of Electrical and Information Engineering, Hunan University, Changsha, China;College of Electrical and Information Engineering, Hunan University, Changsha, China
Venue:
Machine Graphics & Vision International Journal
Year:
2009

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Abstract

The developments of multiresolution analysis, such as the wavelet, curvelet and contourlet transforms, have yielded adequate tools to characterize different scales of textures effectively. These methods exhibit different performances in processing texture images due to their different characteristics. In order to use those complementary characteristics simultaneously, a texture classification method by combining different image decomposition methods is proposed. The proposed method is compared with the methods where only one kind of multiresolution transform is used. The experimental results demonstrate that the combined features can effectively capture the complementary information from different image decomposition methods and obviously improve the texture classification accuracy.