吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 60-63.

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Texture image retrieval based on DT-CWT and generalized gaussian density

ZHANG Jiu-wen, MI Jin-cai, ZHANG Tong-feng   

  1. School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
  • Received:2012-05-19 Published:2013-06-01

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Abstract:

A new texture image retrieval method based on dual tree complex wavelet transform (DT-CWT) and Generalized Gaussian Density was proposed.By using the DT-CWT the query images and target images are decomposed to six directional sub-bands at each level.Modeling the marginal distribution of dual tree complex wavelet coefficients using Generalized Gaussian Density (GGD) to generate texture feature vectors. Kullback-Leibler distance (KLD) function was used as similarity measurement.The experimental results show that this method has higher accuracy than the methods based on energy feature and Euclidean distance as well as methods based on wavelet transform,contourlet transform and others using generalized Gaussian model in the same scale.

Key words: dual-tree complex wavelet transform, deneralized gaussian density, Kullback-Leibler distance, texture image retrieval

CLC Number: 

  • TP391.4

[1] Minh N Do,Martin Vetterli.Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance [C]// IEEE Transactions on Image Processing,2002,111:146-158.

[2] Choy Siu-kai,Tong Chong-kze.Supervised texture classification using characteristic generalized Gaussian density [J].Journal of Mathematical Imaging and Vision,2007,29(1):35-47.

[3] Chen Xin-wu,Ma Jian-zhong.Texture Image Retrieval Based on Contourlet-2.3 and Generalized Gaussian Density Model [C]// 2010 International Conference on Computer Application and System Modeling ICCASM,2010.

[4] Kingsbury N G.Complex wavelets for shift invariant analysis and filtering of signals [J].Appl Comp HarmonicAnal,2000,10(3):234-253.

[5] Kingsbury N G.The dual-tree complex wavelet transform:A new efficient tool for image restoration and enhancement [C]// Proc.European Signal Processing Conf Hodes,1998:319-322.

[6] 曲怀敬.Contourlet变换在纹理图像检索和医学图像分割中的应用研究[D].济南:山东大学,2009. Qu Huai-jin.Research on applications of contourlet transform in texture image retrieval and medical image segmentation[D].Jinan:Shandong University,2009.

[7] Joshi R L,Fisher T R.Comparison of generalized gaussian and laplacian modeling in DCT Image coding [J].IEEE.Signal Processing Letters,1995,2(5):81-82.

[8] Moulin P,Liu J.Analysis of multiresolution image denoisings scheme using generalized Gaussian and complexity priors [J].IEEE Transactions on Information Theory,1999,45(3):909-919.

[9] Cover T M,Thomas J A.Elements of information Theory[M].New York:Wiley,1991:68.
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