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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2007, Vol. 8 Issue (4): 538-549    DOI: 10.1631/jzus.2007.A0538
Information & Computer Science     
Multiwavelets domain singular value features for image texture classification
RAMAKRISHNAN S., SELVAN S.
Department of Information Technology, PSG College of Technology, Coimbatore 641 004, India
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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.

Key words： Image texture classification      Multiwavelets transformation      Probabilistic neural network (PNN)     
Received: 15 August 2006     
CLC:  TP391  
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SELVAN S.
RAMAKRISHNAN S.
Cite this article:

RAMAKRISHNAN S., SELVAN S.. Multiwavelets domain singular value features for image texture classification. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2007, 8(4): 538-549.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2007.A0538     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2007/V8/I4/538

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