Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform (original) (raw)

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.

Access this article

Log in via an institution

Subscribe and save

• Starting from 10 chapters or articles per month
• Access and download chapters and articles from more than 300k books and 2,500 journals
• Cancel anytime View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

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

Download references

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.

Author information

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

Authors

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

Corresponding author

Correspondence toXiang-yang Wang.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical standard

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.

Informed consent

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

Additional information

Communicated by V. Loia.

Rights and permissions

About this article

Cite this article

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

Download citation

• Published: 14 March 2018
• Version of record: 14 March 2018
• Issue date: 01 July 2019
• DOI: https://doi.org/10.1007/s00500-018-3127-8

Keywords