Application of Information Redundancy Measure To Image Segmentation

  • Dmitry MurashovEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 794)


In this paper, the problem of image segmentation quality is considered. The main idea is to find a quality criterion, which could have an extremum. The problem is viewed as selecting the best segmentation from a set of images generated by segmentation algorithm at different parameter values. We propose to use information redundancy measure as a criterion for optimizing segmentation quality. The method for constructing the redundancy measure provides criterion with extremal properties. To show efficiency of the proposed criterion, computing experiment is carried out. The proposed criterion is combined with SLIC and EDISON segmentation algorithms. Computing experiment shows that the segmented image corresponding to a minimum of redundancy measure produces acceptable information distance when compared with the original image. In most cases, the lowest information distance between this segmented image and ground-truth segmentations is obtained. An example of applying the redundancy measure to segmentation of images of painting material cross-sections is considered.


Image segmentation Segmentation quality Redundancy measure Variation of information Painting material cross-sections 



The research was supported in part by the Russian Foundation for Basic Research (grants No. 18-07-01385 and No. 18-07-01231).


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels. Technical report (2010)Google Scholar
  2. 2.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012). Scholar
  3. 3.
    Ana, L.N.F., Jain, A.K.: Robust data clustering. In: 2003 Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 128–133. IEEE (2003).
  4. 4.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011). Scholar
  5. 5.
    Atick, J.J., Redlich, A.N.: Towards a theory of early visual processing. Neural Comput. 2(3), 308–320 (1990). Scholar
  6. 6.
    Barlow, H.B.: Possible principles underlying the transformations of sensory messages. Sens. Commun., 217–234 (1961). Scholar
  7. 7.
    Beneš, M., Zitová, B., Hradilová, J., Hradil, D.: Image processing in material analyses of artworks. In: Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP), pp. 521–524 (2008).
  8. 8.
    Christoudias, C.M., Georgescu, B., Meer, P.: Synergism in low level vision. In: 16th International Conference on Pattern Recognition, ICPR 2002, Quebec, Canada, 11–15 August 2002, pp. 150–155 (2002).
  9. 9.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002). Scholar
  10. 10.
    Csurka, G., Larlus, D., Perronnin, F., Meylan, F.: What is a good evaluation measure for semantic segmentation? In: BMVC, vol. 27, pp. 32.1–32.11 (2013).
  11. 11.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59(2), 167–181 (2004). Scholar
  12. 12.
    Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983). Scholar
  13. 13.
    Frosio, I., Ratner, E.R.: Adaptive segmentation based on a learned quality metric. In: VISAPP 2015, vol. 1, pp. 283–292 (2015).
  14. 14.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Prentice Hall, Upper Saddle River (2008)Google Scholar
  15. 15.
    Haindl, M., Mikeš, S., Pudil, P.: Unsupervised hierarchical weighted multi-segmenter. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 272–282. Springer, Heidelberg (2009). Scholar
  16. 16.
    Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Comput. Vis. Graph. Image Process. 29(1), 100–132 (1985). Scholar
  17. 17.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River (1998)zbMATHGoogle Scholar
  18. 18.
    Kaspar, R., Petru, L., Zitová, B., Flusser, J., Hradilova, J.: Microscopic cross-sections of old artworks. In: 2005 IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. II-578. IEEE (2005).
  19. 19.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: 2001 Proceedings of Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 416–423. IEEE (2001).
  20. 20.
    Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004). Scholar
  21. 21.
    Meer, P., Georgescu, B.: Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Mach. Intell. 23(12), 1351–1365 (2001). Scholar
  22. 22.
    Meilă, M.: Comparing clusterings by the variation of information. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT-Kernel 2003. LNCS (LNAI), vol. 2777, pp. 173–187. Springer, Heidelberg (2003). Scholar
  23. 23.
    Meilă, M.: Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 577–584. ACM (2005).
  24. 24.
    Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971). Scholar
  25. 25.
    Unnikrishnan, R., Pantofaru, C., Hebert, M.: A measure for objective evaluation of image segmentation algorithms. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) - Workshops, CVPR 2005, vol. 03, pp. 34–41. IEEE Computer Society, Washington, DC (2005).
  26. 26.
    Wagner, S., Wagner, D.: Comparing clusterings - an overview. Technical report 2006–04, Universität Karlsruhe (TH) (2007)Google Scholar
  27. 27.
    Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008). Scholar
  28. 28.
    Zitová, B., Beneš, M., Hradilová, J., Hradil, D.: Analysis of painting materials on multimodal microscopic level. In: IS&T/SPIE Electronic Imaging, pp. 75,310F–1–75,310F–9. International Society for Optics and Photonics (2010).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federal Research Center “Computer Science and Control” of RASMoscowRussia

Personalised recommendations