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Image Similarity in Gaussian Mixture Model Based Image Retrieval

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Image Processing and Communications Challenges 8 (IP&C 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 525))

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Abstract

As color is a useful characteristic of our surrounding world, it gives clue for the recognition, indexing and retrieval of the images presenting the visual similarity. Thus, this paper focuses on the proper choice of the similarity measure applied to compare features evaluated in process the modeling of lossy coded color image information, based on the mixture approximation of chromaticity histogram. The analyzed similarity measure are those based on \(Kullback-Leibler\) Diverence, as Goldberger approximation and Variational approximation. Signature-based distance function as Hausdorff Distance, Perceptually Modified Hausdorff Distance and Earth \(Mover's\) Distance were also investigated. Retrieval results were obtained for RGB, I1I2I3, YUV, CIE XYZ, CIE \(L^*a^*b^*\), HSx, LSLM and TSL color spaces.

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References

  1. Ohta, Y., Kanade, T., Sakai, T.: Color information for region segmentation. Comput. Graph. Image Processing. 13(3), 222–241 (1980)

    Article  Google Scholar 

  2. Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)

    Article  Google Scholar 

  3. Fairchild, M.D.: Color and Image Appearance Models, Color Appearance Models. Wiley, New York (2005)

    Google Scholar 

  4. McLachlan, G., Peel, D.: Finite Mixtures Models. Wiley, New York (2000)

    Book  MATH  Google Scholar 

  5. Jeong, S., Won, C.S., Gray, R.M.: Image retrieval using color histograms generated by gauss mixture vector quantization. Comp. Vis. Image Underst. 94(1–3), 44–66 (2004)

    Article  Google Scholar 

  6. Xing, X., Zhang, Y., Gong, B.: Mixture model based contextual image retrieval. In: CIVR 2010, pp. 251–258 (2010)

    Google Scholar 

  7. Luszczkiewicz, M., Smolka, B.: Application of bilateral filtering and Gaussian mixture modeling for the retrieval of paintings. In: Proceedings of 16th IEEE International Conference on Image Processing (ICIP), pp. 77–80 (2009)

    Google Scholar 

  8. Luszczkiewicz-Piatek, M., Smolka, B.: Selective color image retrieval based on the gaussian mixture model. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds.) ACIVS 2012. LNCS, vol. 7517, pp. 431–443. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33140-4_38

    Chapter  Google Scholar 

  9. Goldberger, J., et al.: An efficient image similarity measure based on approximation of KL divergence between two gaussian mixtures. In: ICCV, pp. 487–493 (2003)

    Google Scholar 

  10. Harshey, J., Olsen, P.: Approximating the kullback-leibler divergence between gaussian mixure models. In: Proceedings of IEEE ICASSP, vol. 4, pp. 317–320 (2007)

    Google Scholar 

  11. Huo, Q., Li, W.: A DTW-based dissimilarity measure for left-to-right hidden Markov models and its application to word confusability analysis. In: INTERSPEECH, pp. 2338–2341 (2006)

    Google Scholar 

  12. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  13. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Analy. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  14. Park, B.G., Lee, K.M., Lee, S.U.: Color-based image retrieval using perceptually modified hausdorff distance. EURASIP J. Image Video Proces. 4, 1–10 (2008)

    Google Scholar 

  15. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1990)

    MATH  Google Scholar 

  16. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics - sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intel. 9, 947–963 (2001)

    Article  Google Scholar 

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Acknowledgment

This work has been supported by The National Science Centre under SONATA grant no. UMO-2011/01/D/ST6/04554.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Department of Applied Computer Science, University of Lodz, Banacha 22, 90-238, Lodz, Poland

    Maria Luszczkiewicz-Piatek

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  1. Maria Luszczkiewicz-Piatek
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Correspondence to Maria Luszczkiewicz-Piatek .

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Editors and Affiliations

  1. Institute of Telecommunications and Computer Sciences, UTP University of Science and Technology, Bydgoszcz, Poland

    Ryszard S. Choraś

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Luszczkiewicz-Piatek, M. (2017). Image Similarity in Gaussian Mixture Model Based Image Retrieval. In: Choraś, R. (eds) Image Processing and Communications Challenges 8. IP&C 2016. Advances in Intelligent Systems and Computing, vol 525. Springer, Cham. https://doi.org/10.1007/978-3-319-47274-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-47274-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47273-7

  • Online ISBN: 978-3-319-47274-4

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