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Texture Image Retrieval Based on Contourlet Transform and Active Perceptual Similarity Learning

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Advanced Data Mining and Applications (ADMA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5139))

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Abstract

This paper proposes a new texture image retrieval scheme based on contourlet transform and support vector machines (SVMs). In the scheme, the energies and the generalized Gaussian distribution (GGD) parameters are used to represent the contourlet subband features. Using the representations, a two-run SVM retrieval algorithm which employs an one-class SVM followed by a two-class SVM is proposed to carry out the perceptual similarity measurement. For the query image, the one-class SVM is used to obtain the effective initial training set with positive and negative samples. Using these initial samples, the two-class SVM is applied to refine on the image classification subject to the user’s relevance feedback. Compared with existing texture image retrieval methods, the proposed retrieval scheme is demonstrated respectively to be effective on the VisTex database of 640 texture images and the Brodatz database of 1760 texture images. Experimental results have shown that the proposed retrieval scheme can attain 99.38% and 98.07% of the average rates respectively for the two databases.

This project is sponsored by SRF for ROCS, SEM (2004.176.4) and NSF SD Province (Z2004G01) of China.

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References

  1. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)

    Article  Google Scholar 

  2. Smith, J.R., Chang, S.–F.: Automated binary texture feature sets for image retrieval. In: Proc. ICASSP 1996, Atlanta, GA (1996)

    Google Scholar 

  3. Randen, T., Husoy, J.H.: Filtering for texture classification: A comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(4), 291–310 (1999)

    Article  Google Scholar 

  4. Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing 14(12), 2091–2106 (2005)

    Article  MathSciNet  Google Scholar 

  5. Qu, H., Peng, Y., Sun, W.: Texture Image Retrieval Based on Contourlet Coefficient Modeling with Generalized Gaussian Distribution. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 493–502. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Kokare, M., Chatterji, B.N., Biswas, P.K.: Comparison of similarity metrics for texture image retrieval. In: Proceedings of IEEE Conference on Convergent Technologies for Asia-Pacific Region, vol. 2, pp. 571–575 (2003)

    Google Scholar 

  7. Do, M.N., Vetterli, M.: Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Transactions on Image Processing 11(2), 146–158 (2002)

    Article  MathSciNet  Google Scholar 

  8. Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Video Technology 8(5), 644–655 (1998)

    Article  Google Scholar 

  9. Tian, Q., Hong, P., Huang, T.S.: Update relevant image weights for content-based image retrieval using support vector machines. In: Proceedings of IEEE International Conference on Multimedia and Expo., Hilton New York & Towers, New York, vol. 2, pp. 1199–1202 (2000)

    Google Scholar 

  10. Chen, Y., Zhou, X.S., Huang, T.S.: One-class SVM for learning in image retrieval. In: Proceedings of IEEE International Conference on Image Processing, Thessaloniki, Greece, vol. 1, pp. 34–37 (2001)

    Google Scholar 

  11. Vasconcelos, N., Lippman, A.: A probabilistic architecture for content-based image retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA, vol. 1, pp. 216–221 (2000)

    Google Scholar 

  12. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  13. Li, S., Kwok, J.T., Zhu, H., Wang, Y.: Texture classification using the support vector machines. Pattern Recognition 36, 2883–2893 (2003)

    Article  MATH  Google Scholar 

  14. Scholkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation 13(7), 1443–1471 (2001)

    Article  Google Scholar 

  15. Fletcher, R.: Practical Methods of Optimization, 2nd edn. John Wiley & Sons, New York (1987)

    MATH  Google Scholar 

  16. Qu, H., Wu, Y., Peng, Y.: Contourlet Coefficient Modeling with Generalized Gaussian Distribution and Application. In: Proceeding of International Conference on Audio, Language and Image Processing, Shanghai, China (to appear, 2008)

    Google Scholar 

  17. Brodatz, P.: Texture: A Photograph Album for Artists and Designers. Dover, New York (1966)

    Google Scholar 

  18. Daubechies, I.: Ten lectures on wavelet. SIAM, Philadelphia (1992)

    Google Scholar 

  19. Korfhage, R.R.: Information storage and Retrieval. John Wiley & Sons, New York (1997)

    Google Scholar 

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Qu, H., Peng, Y., Wan, H., Han, M. (2008). Texture Image Retrieval Based on Contourlet Transform and Active Perceptual Similarity Learning. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_33

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  • DOI: https://doi.org/10.1007/978-3-540-88192-6_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88191-9

  • Online ISBN: 978-3-540-88192-6

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