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Evaluation of Distances Between Color Image Segmentations

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Pattern Recognition and Image Analysis (IbPRIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3523))

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Abstract

We illustrate the problem of comparing images by means of their color segmentations. A group of seven distances are proposed within the frame of the Integrated Region Matching distance and the employ of Multivariate Gaussian Distributions (MGD) for the color description of image regions. The performance of these distances is examined in tasks such as image retrieval and object recognition using the two segmentation algorithms in [1] and [2]. The best overall results are obtained for both tasks using the graph–partition approach along with the Fréchet distance, outperforming other distances in comparing MGDs.

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References

  1. Vergés-Llahí, J., Climent, J., Sanfeliu, A.: Colour image segmentation solving hardconstraints on graph-partitioning greedy algorithm. In: Proc. 15th International Conference on Pattern Recognition, ICPR 2000, vol. 3, pp. 629–632 (2000)

    Google Scholar 

  2. Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 381–396 (2002)

    Article  Google Scholar 

  3. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Dim, B., Huang, Q., Gorkani, M., Hafner, J., Lee, D., Petkovick, D., Steele, D., Yanker, P.: Query by image and video content: The QBIC system. Computer 28, 23–32 (1995)

    Article  Google Scholar 

  4. Pentland, A., Picard, R., Sclaroff, S.: Photobook: Tools for content-based manipulation of image databases. In: Proc. SPIE, vol. 2185, pp. 34–47 (1994)

    Google Scholar 

  5. Gupta, A., Jain, R.: Visual information retrieval. Comm. ACM 40, 69–79 (1997)

    Article  Google Scholar 

  6. Smith, J., Chang, S.: VisualSEEK: A fully automated content-based query system. In: Proc. ACM Multimedia, pp. 87–98 (1996)

    Google Scholar 

  7. Wang, J., Wiederhold, G., Firschein, O., Sha, X.: Content-based image indexing and searching using Daubechies’ wavelets. Int’l Digital Libraries 1, 311–328 (1998)

    Article  Google Scholar 

  8. Natsev, A., Rastogi, R., Shim, K.: WALRUS: A similarity retrieval algorithm for images databases. IEEE Trans. on Knowledge and Data Eng. 16, 301–316 (2004)

    Article  Google Scholar 

  9. Ma, W., Manjunath, B.: NeTra: A toolbox for navigating large image database. In: Proc. IEEE Int’l Conf. Image Processing, pp. 568–571 (1997)

    Google Scholar 

  10. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 1026–1038 (2002)

    Article  Google Scholar 

  11. Wang, J., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 947–963 (2001)

    Article  Google Scholar 

  12. Nascimento, M., Sridhar, V., Li, X.: Effective and efficient region-based image retrieval. Journal of Visual Languages and Computing 14, 151–179 (2003)

    Article  Google Scholar 

  13. Dowson, D., Landau, B.: The Fréchet distance between multivariate normal distributions. Journal of Multivariate Analysis 12, 450–455 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  14. Bhattacharyya, A.: On a measure of diverg. between two statistical populations defined by their probability distrib. Bull. Calcutta Math. Soc. 35, 99–110 (1943)

    MATH  MathSciNet  Google Scholar 

  15. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)

    Article  Google Scholar 

  16. Kondor, R., Jebara, T.: A kernel between sets of vectors. In: Proc. Int. Conf. on Machine Learning, ICML 2003 (2003)

    Google Scholar 

  17. Kullback, S.: Information Theory and Statistics. Dover, New York (1968)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institut de Robòtica i Informàtica Industrial, Technological Park of Barcelona, U Building,  

    Jaume Vergés-Llahí & Alberto Sanfeliu

Authors
  1. Jaume Vergés-Llahí
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  2. Alberto Sanfeliu
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Editor information

Editors and Affiliations

  1. Instituto Superior Técnico & Instituto de Sistemas e Robótica,, 1049-001, Lisboa, Portugal

    Jorge S. Marques

  2. ETSI Informática y e Telecomunicación, University of Granada, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  3. Instituto Superior Técnico, CERENA-Centro de Recursos Naturais e Ambiente, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Pedro Pina

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© 2005 Springer-Verlag Berlin Heidelberg

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Vergés-Llahí, J., Sanfeliu, A. (2005). Evaluation of Distances Between Color Image Segmentations. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_33

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  • DOI: https://doi.org/10.1007/11492542_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26154-4

  • Online ISBN: 978-3-540-32238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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