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基于雙廣義高斯模型和多尺度融合的紋理圖像檢索方法

楊娟,  李永福,  汪榮貴,  薛麗霞,  張清楊

楊娟, 李永福, 汪榮貴, 薛麗霞, 張清楊. 基于雙廣義高斯模型和多尺度融合的紋理圖像檢索方法[J]. 電子與信息學報, 2016, 38(11): 2856-2863. doi: 10.11999/JEIT160181
引用本文: 楊娟, 李永福, 汪榮貴, 薛麗霞, 張清楊. 基于雙廣義高斯模型和多尺度融合的紋理圖像檢索方法[J]. 電子與信息學報, 2016, 38(11): 2856-2863. doi: 10.11999/JEIT160181
YANG Juan, LI Yongfu, WANG Ronggui, XUE Lixia, ZHANG Qingyang. Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2856-2863. doi: 10.11999/JEIT160181
Citation: YANG Juan, LI Yongfu, WANG Ronggui, XUE Lixia, ZHANG Qingyang. Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2856-2863. doi: 10.11999/JEIT160181

基于雙廣義高斯模型和多尺度融合的紋理圖像檢索方法

doi: 10.11999/JEIT160181
基金項目: 

中國博士后基金(2014M561817)，安徽省自然科學基金(J2014AKZR0055)

Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion

Funds: 

China Postdoctoral Fund (2014M561817), The Natural Science Foundation of Anhui Province (J2014AKZR 0055)

  • 摘要: 紋理因素是描述圖像的重要特征之一，為了準確地刻畫紋理特征，增強圖像的區(qū)分能力，該文提出一種基于雙樹復(fù)數(shù)小波域統(tǒng)計特征的紋理圖像檢索方法。首先對圖像采用雙樹復(fù)數(shù)小波變換得到各子帶系數(shù)，由于系數(shù)存在細微不完全對稱分布特性，將其建模為雙廣義高斯模型。其次，因為各子帶系數(shù)之間不完全獨立也不完全沖突，存在不確定關(guān)系，所以采用模糊集合和證據(jù)理論(FS-DS)的方法，融合各子帶系數(shù)特征。最后，對Brodatz和彩色紋理圖像庫進行仿真實驗，并與多種統(tǒng)計建模的方法相比較。結(jié)果表明，該方法有效地提高了紋理圖像的平均檢索率。
    Abstract: Texture factor is one of the most important characteristics in the image description. In order to describe the texture feature accurately, and enhance image distinguish ability, a method of texture image retrieval is proposed based on Dual-Tree Complex Wavelet Transform (DT-CWT) in this paper. Firstly, each sub-band coefficient is obtained by DT-CWT, because the coefficient distribution exists slight incomplete symmetrical feature, which is modeled as dual-generalized Gaussian model. Secondly, there is incomplete independent and uncertain conflict between the sub-band coefficients, therefore the Fuzzy Set and Dempster-Shafer (FS-DS) evidence theory are applied to blending the characteristics of each subband coefficients. The performance of the propose algorithm is tested on the Brodatz and color texture image library, and also compared with a variety of statistical modeling methods. The experimental results demonstrate that the proposed method can improve the average retrieval rate of the texture images effectively.
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  • GUO Jingming and PRASETYO H. Content-based image retrieval using features extracted from halftoning-based block truncation coding[J]. IEEE Transactions on Image Processing, 2015, 24(3): 1010-1024. doi: 10.1109/TIP.2014. 2372619.
    GUO Xian, HUANG Xin, and ZHANG Liangpei. Three- dimensional wavelet texture feature extraction and classification for multi/hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2183-2187. doi: 10.1109/LGRS.2014.2323963.
    HONG Xiaopeng, ZHAO Guoying, PIETIKAINEN M, et al. Combining LBP difference and feature correlation for texture description[J]. IEEE Transactions on Image Processing, 2014, 23(6): 2557-2568. doi: 10.1109/TIP.2014.2316640.
    ARASHLOO S R and KITTLER J. Dynamic texture recognition using multiscale binarized statistical image features[J]. IEEE Transactions on Multimedia, 2014, 16(8): 2099-2109. doi: 10.1109/TMM.2014.2362855.
    HARALICK R M, SHANMUGAM K, and DINSTEIND I. Texture feature for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, 3(6): 610-621.
    LI Chaorong, DUAN Guiduo, and ZHONG Fujin. Rotation invariant texture retrieval considering the scale dependence of Gabor wavelet[J]. Transactions on Image Processing, 2015, 24(8): 2344-2354. doi: 10.1109/TIP.2015.2422575.
    RIAZ F, HASSAN A, REHAN S, et al. Texture classification using rotation- and scale-invariant Gabor texture features[J]. IEEE Signal Processing Letters, 2013, 20(6): 607-610. doi: 10.1109/LSP.2013.2259622.
    SCHARFENBERGER C, WONG A, and DAVID A. Clausi. Structure-guided statistical textural distinctiveness for salient region detection in natural images[J]. IEEE Transactions on Image Processing, 2015, 24(1): 457-470. doi: 10.1109/TIP.2014.2380351.
    KINGABURY N. Complex wavelets for shift inavriant analysis and filtering of Signals[J]. Journal of Applied and Computational Harmonic Analysis, 2001, 10(3): 234-253.
    ANANTRASIRICHAI N, BURN J, and BULL D R. Robust texture features based on undecimated dual-tree complexwavelets and local magnitude binary patterns[C]. 2015 IEEE International Conference on Image Processing (ICIP), Qubec, 2015: 3957-3961.
    DO M N and VETTLI M. Wavelet-based texture retrieval using generalized gaussian density and kullbackleiber distance[J]. IEEE Transactions on Image Processing, 2002, 11(2): 146-158. doi: 10.1109/83.982822.
    LASMAR N E and BERTHOUMIEU Y. Gaussian copula multivariate modeling for texture image retrieval using wavelet transforms[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2246-2261. doi: 10.1109/TIP.2014. 2313232.
    ARANI M N and ZHANG Xiaoping. Generalized Gaussian mixture conditional random field model for image labeling[C]. 2014 IEEE Global Conference on, Signal and Information Processing (GlobalSIP), Atlanta GA, 2014: 1068-1072.
    ALLILI M S, BAAZIZ N, and MEJRI M. Texture modeling using contourlets and finite mixtures of generalized Gaussian distributions and applications[J]. IEEE Transactions on Multimedia, 2014, 16(3): 772-784. doi: 10.1109/TMM.2014. 2298832.
    CHOY S K and TONG C S. Statistical wavelet subband characterization based on generalized Gamma density and its application in texture retrieval[J]. IEEE Transactions on Image Processing, 2010, 19(2): 281-289. doi: 10.1109/TIP. 2009.2033400.
    KWITT R, MEERWALD P, and UHL A. Efficient texture image retrieval using copulas in a Bayesian framework[J]. IEEE Transactions on Image Processing, 2011, 20(7): 2063-2077. doi: 10.1109/TIP.2011.2108663.
    COMBREXELLE S, WENDT H, DOBIGEON N, et al. Bayesian estimation of the multifractality parameter for image texture using a whittle approximation[J]. IEEE Transactions on Image Processing, 2015, 24(8): 2540-2551. doi: 10.1109/TIP.2015.2426021.
    WANG Taiyue, LI Hongwei, LI Zhiming, et al. A fast parameter estimation of generalized Gaussian distribution[C]. 2006 8th International Conference on Signal Processing, Beijing, 2006, 1: 1-4. doi: 10.1109/ICOSP.2006.345546.
    韓峰, 楊萬海, 袁曉光. 基于模糊集合的證據(jù)理論信息融合方法[J]. 控制與決策, 2010, 25(3): 449-452. doi: 10.13195/j.cd. 2010.03.132.hanf.023.
    HAN Feng, YANG Wanhai, and YUAN Xiaoguang. Evidence theory information fusion method based on fuzzy set[J]. Control and Decision, 2010, 25(3): 449-452. doi: 10.13195/ j.cd.2010.03.132.hanf.023.
    DANIEL C. On the solution of linear differential equations with singular coefficients[J]. Journal of Differential Equations, 1982, 46(2): 310-323.
    KWITT R and UHL A. Lightweight probabilistic texture retrieval[J]. IEEE Transactions on Image Processing, 2010, 19(1): 241-253. doi: 10.1109/TIP.2009.2032313.
    MANJUNATH B S and MA W Y. Texture feature features for browsing and retrieval of image data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(8): 837-842. doi: 10.1109/34.531803.
    QU H J, PENG Y H, and SUN W F. Texture image retrival based on Contourlet coefficient modeling with generalized Gaussian distribution[J]. LNCS, 2007, 4683: 493-502.
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出版歷程
  • 收稿日期:  2016-03-01
  • 修回日期:  2016-07-01
  • 刊出日期:  2016-11-19

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