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Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform

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Abstract

In this paper, we proposed a new framework for textured image retrieval, which is based on Weibull statistical distribution and nonsubsampled contourlet transform. Firstly, the image is decomposed into one lowpass subband and several highpass subbands by using nonsubsampled contourlet transform (NSCT). Secondly, Weibull probability distribution is employed to describe the statistical characteristics of the highpass NSCT coefficients, and the Weibull model parameters are utilized to construct a compact texture image feature space. Finally, image similarity measurement is accomplished by using closed-form solutions for the Kullback–Leibler divergences between the Weibull statistical models. Experimental results demonstrate the high efficiency of our textured image retrieval scheme, which can provide better retrieval rates and lower computational cost, in comparison with the state-of-the-art approaches recently proposed in the literature.

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References

  • Allili MS (2012) Wavelet modeling using finite mixtures of generalized Gaussian distributions: application to texture discrimination and retrieval. IEEE Trans Image Process 21(4):1452–1464

    Article  MathSciNet  MATH  Google Scholar 

  • Allili MS, Baaziz N, Mejri M (2014) Texture modeling using contourlets and finite mixtures of generalized Gaussian distributions and applications. IEEE Trans Multimed 16(3):772–784

    Article  Google Scholar 

  • Almalki SJ, Nadarajah S (2014) A new discrete modified Weibull distribution. IEEE Trans Reliab 63(1):68–80

    Article  Google Scholar 

  • Alzu’bi A, Amira A, Ramzan N (2015) Semantic content-based image retrieval: a comprehensive study. J Vis Commun Image Represent 32:20–54

    Article  Google Scholar 

  • Amsterdam Library of Textures [Online] (2017) http://staff.science.uva.nl/~aloi/public_alot. Accessed 22 May 2017

  • Brodatz Texture Image Database [Online] (2017) http://www.ux.uis.no/~tranden/brodatz.html. Accessed 22 May 2017

  • Celik C, Bilge HS (2017) Content based image retrieval with sparse representations and local feature descriptors: a comparative study. Pattern Recogn 68:1–13

    Article  Google Scholar 

  • Choy SK, Tong CS (2010) Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval. IEEE Trans Image Process 19(2):281–289

    Article  MathSciNet  MATH  Google Scholar 

  • Da CA, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101

    Article  Google Scholar 

  • Datta R, Joshi D, Li J et al (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):5:1–5:60

    Article  Google Scholar 

  • Do MN, Vetterli M (2002) Wavelet-based texture retrieval using generalized Gaussian density and Kullback–Leibler distance. IEEE Trans Image Process 11(2):146–158

    Article  MathSciNet  Google Scholar 

  • Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    Article  MathSciNet  MATH  Google Scholar 

  • Juan YANG, Yongfu LI, Ronggui WANG, Lixia XUE, Qingyang ZHANG (2016) Texture image retrieval method based on dual-generalized Gaussian model and multi-scale fusion. J Electron Inf Technol 38(11):2856–2863

    Google Scholar 

  • Kwitt R, Meerwald P, Uhl A (2011) Efficient texture image retrieval using copulas in a Bayesian framework. IEEE Trans Image Process 20(7):2063–2077

    Article  MathSciNet  MATH  Google Scholar 

  • Kwitt R, Uhl A (2008) Image similarity measurement by Kullback-Leibler divergences between complex Wavelet subband statistics for texture retrieval. In: Proceedings of IEEE international conference on image processing (ICIP), San Diego, California, USA, pp 933–936

  • Kwitt R, Uhl A (2009) A joint model of complex wavelet coefficients for texture retrieval. In: Proceedings of the IEEE international conference on image processing (ICIP ’09), Cairo, Egypt, pp 1877–1880

  • Lasmar NE, Berthoumieu Y (2014) Gaussian copula multivariate modeling for texture image retrieval using wavelet transforms. IEEE Trans Image Process 23(5):2246–2261

    Article  MathSciNet  MATH  Google Scholar 

  • Li C, Li J, Fu B (2013) Magnitude-phase of quaternion wavelet transform for texture representation using multilevel copula. IEEE Signal Process Lett 20(8):799–802

    Article  Google Scholar 

  • Li C, Duan G, Zhong F (2015) Rotation invariant texture retrieval considering the scale dependence of Gabor wavelet. IEEE Trans Image Process 24(8):2344–2354

    Article  MathSciNet  MATH  Google Scholar 

  • Li X, Uricchio T, Ballan L et al (2016) Socializing the semantic gap: a comparative survey on image tag assignment, refinement, and retrieval. ACM Comput Surv 49(1):14:1–14:39

    Article  Google Scholar 

  • Li C, Huang Y, Zhu L (2017) Color texture image retrieval based on Gaussian copula models of Gabor wavelets. Pattern Recogn 64:118–129

    Article  Google Scholar 

  • Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

    Article  Google Scholar 

  • Piras L, Giacinto G (2017) Information fusion in content based image retrieval: a comprehensive overview. Inf Fusion 37:50–60

    Article  Google Scholar 

  • Rami H, Belmerhnia L, Maliani AD (2016) Texture retrieval using mixtures of generalized Gaussian distribution and Cauchy-Schwarz divergence in wavelet domain. Sig Process Image Commun 42:45–58

    Article  Google Scholar 

  • Salzburg Textures [Online] (2017) http://wavelab.at/sources/STex. Accessed 22 May 2017

  • Seetharaman K (2015) Image retrieval based on micro-level spatial structure features and content analysis using full range Gaussian Markov random field model. Eng Appl Artif Intell 40:103–116

    Article  Google Scholar 

  • Tzagkarakis G, Beferull-Lozano B, Tsakalides P (2006) Rotation-invariant texture retrieval with gaussianized steerable pyramids. IEEE Trans Image Process 15(9):2702–2718

    Article  Google Scholar 

  • Vasconcelos N, Lippman A (1997) Library-based coding: a representation for efficient video compression and retrieval. In: Proceedings of data compression conference (DCC’97), Snowbird, Utah, USA, pp 121–130

  • Ves ED, Acevedo D, Ruedin A, Benavent X (2014) A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval. Pattern Recogn 47(9):2925–2939

    Article  Google Scholar 

  • Vo A, Oraintara S (2010) A study of relative phase in complex wavelet domain: property, statistics and applications in texture image retrieval and segmentation. Sig Process Image Commun 25(1):28–46

    Article  Google Scholar 

  • Yuan H, Zhang XP (2010) Statistical modeling in the wavelet domain for compact feature extraction and similarity measure of images. IEEE Trans Circuits Syst Video Technol 20(3):439–445

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61472171, 61272416 and 61701212, Project funded by China Postdoctoral Science Foundation No. 2017M621135, and the Natural Science Foundation of Liaoning Province of China under Grant No. 201602463.

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China

    Hong-ying Yang, Lin-lin Liang, Can Zhang, Xue-bing Wang, Pan-pan Niu & Xiang-yang Wang

  2. Department of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, People’s Republic of China

    Pan-pan Niu

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  1. Hong-ying Yang
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  2. Lin-lin Liang
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  3. Can Zhang
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  4. Xue-bing Wang
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  5. Pan-pan Niu
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  6. Xiang-yang Wang
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Correspondence to Xiang-yang Wang.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Communicated by V. Loia.

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Yang, Hy., Liang, Ll., Zhang, C. et al. Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform. Soft Comput 23, 4749–4764 (2019). https://doi.org/10.1007/s00500-018-3127-8

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  • DOI: https://doi.org/10.1007/s00500-018-3127-8

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