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Received: 2006-04-26

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.9 P.1516-1521


Texture classification based on EMD and FFT

Author(s):  XIONG Chang-zhen, XU Jun-yi, ZOU Jian-cheng, QI Dong-xu

Affiliation(s):  School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510275, China; more

Corresponding email(s):   xczkiong@yahoo.com

Key Words:  Texture classification, Empirical mode decomposition (EMD), Fourier transform, Auto-registration, Rotation-invariant

XIONG Chang-zhen, XU Jun-yi, ZOU Jian-cheng, QI Dong-xu. Texture classification based on EMD and FFT[J]. Journal of Zhejiang University Science A, 2006, 7(9): 1516-1521.

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%DOI 10.1631/jzus.2006.A1516

T1 - Texture classification based on EMD and FFT
A1 - XIONG Chang-zhen
A1 - XU Jun-yi
A1 - ZOU Jian-cheng
A1 - QI Dong-xu
J0 - Journal of Zhejiang University Science A
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SP - 1516
EP - 1521
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A1516

empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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