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Received: 2006-08-15

Revision Accepted: 2007-01-05

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.4 P.538~549

http://doi.org/10.1631/jzus.2007.A0538


Multiwavelets domain singular value features for image texture classification


Author(s):  RAMAKRISHNAN S., SELVAN S.

Affiliation(s):  Department of Information Technology, PSG College of Technology, Coimbatore 641 004, India

Corresponding email(s):   ram_f77@yahoo.com, drselvan@ieee.org

Key Words:  Image texture classification, Multiwavelets transformation, Probabilistic neural network (PNN)


RAMAKRISHNAN S., SELVAN S.. Multiwavelets domain singular value features for image texture classification[J]. Journal of Zhejiang University Science A, 2007, 8(4): 538~549.

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author="RAMAKRISHNAN S., SELVAN S.",
journal="Journal of Zhejiang University Science A",
volume="8",
number="4",
pages="538~549",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0538"
}

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%A RAMAKRISHNAN S.
%A SELVAN S.
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0538

TY - JOUR
T1 - Multiwavelets domain singular value features for image texture classification
A1 - RAMAKRISHNAN S.
A1 - SELVAN S.
J0 - Journal of Zhejiang University Science A
VL - 8
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SP - 538
EP - 549
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A0538


Abstract: 
A new approach based on multiwavelets transformation and singular value decomposition (SVD) is proposed for the classification of image textures. Lower singular values are truncated based on its energy distribution to classify the textures in the presence of additive white Gaussian noise (AWGN). The proposed approach extracts features such as energy, entropy, local homogeneity and max-min ratio from the selected singular values of multiwavelets transformation coefficients of image textures. The classification was carried out using probabilistic neural network (PNN). Performance of the proposed approach was compared with conventional wavelet domain gray level co-occurrence matrix (GLCM) based features, discrete multiwavelets transformation energy based approach, and HMM based approach. Experimental results showed the superiority of the proposed algorithms when compared with existing algorithms.

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

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