Advertisement

Graph Approach in Image Segmentation

Conference paper
  • 719 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 642)

Abstract

In this paper we discuss about graph approach in image segmentation. In first place, some main image processing techniques are classified based upon the output these methods provide. Then, a fuzzy image segmentation definition is presented because in the literature review was found that it was not clearly defined. This definition of fuzzy image segmentation is then related to a hierarchical image segmentation procedure, so this concept is also formally defined in this work. As every output of an image processing algorithm has to be evaluated, then a method to evaluate a hierarchical segmentation output is proposed in order to later propose a method to evaluate a fuzzy image segmentation output. Computational experiences point to some advantages of the proposed hierarchical image segmentation procedure over other algorithms.

Keywords

Graph approach Hierarchical segmentation Edge detection Benchmarking Fuzzy sets 

Notes

Acknowledgments

This research has been partially supported by the Government of Spain, grant TIN2015-66471-P, and by the Government of the Community of Madrid, grant S2013/ICE-2845 (CASI-CAM-CM).

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Basavaprasad, B., Ravindra, H.: A survey on traditional and graph theoretical techniques for image segmentation. In: IJCA Proceedings on National Conference on Recent Advances in Information Technology, NCRAIT, vol. 1, pp. 38–46 (2014)Google Scholar
  3. 3.
    Beutel, J., Kundel, H., Van Metter, R.: Handbook of Medical Imaging. Physics and Psychophysics, vol. 1. SPIE Press (2000)Google Scholar
  4. 4.
    Bezdek, J., Chandrasekhar, R., Attikouzel, Y.: A geometric approach to edge detection. IEEE Trans. Fuzzy Syst. 6, 52–75 (1998)CrossRefGoogle Scholar
  5. 5.
    Bustince, H., Barrenechea, E., Fernández, J., Pagola, M., Montero, J., Guerra, C.: Contrast of a fuzzy relation. Inf. Sci. 180(8), 1326–1344 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Estrada, F., Jepson, A.: Benchmarking image segmentation algorithms. Int. J. Comput. Vis. 85(2), 167–181 (2009)CrossRefGoogle Scholar
  7. 7.
    Gómez, D., Zarrazola, E., Yáñez, J., Rodríguez, J.T., Montero, J.: A new concept of fuzzy image segmentation. In: WSPC, Proceedings of the 11th International FLINS Conference on Decision Making and Soft Computing (FLINS), pp. 17–20 (2014)Google Scholar
  8. 8.
    Gómez, D., Zarrazola, E., Yáñez, J., Montero, J.: A divide-and-link algorithm for hierarchical clustering in networks. Inf. Sci. 316, 308–328 (2015)CrossRefGoogle Scholar
  9. 9.
    Gómez, D., Yáñez, J., Guada, C., Rodríguez, J.T., Montero, J., Zarrazola, E.: Fuzzy image segmentation based upon hierarchical clustering. Knowl. Based Syst. 87, 26–37 (2015)CrossRefGoogle Scholar
  10. 10.
    Guada, C., Gómez, D., Rodríguez, J.T., Yáñez, J., Montero, J.: A Fuzzy Edge-Based Image Segmentation Approach, pp. 1216–1222. Atlantis Press (IFSA-EUSFLAT) (2015)Google Scholar
  11. 11.
    Guada, C., Gómez, D., Rodríguez, J.T., Yáñez, J., Montero, J.: Classifying image analysis techniques from their output. Int. J. Comput. Intell. Syst. 9(1), 43–68 (2016)CrossRefGoogle Scholar
  12. 12.
    Lu, J., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. Appl. 28(5), 823–870 (2007)CrossRefGoogle Scholar
  13. 13.
    Marr, D., Hildreth, E.: Theory of edge detection. Proc. Roy. Soc. London Ser. B Biol. Sci. 207(1167), 187–217 (1980)CrossRefGoogle Scholar
  14. 14.
    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: Proceedings of the 8th International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  15. 15.
    Nachtegael, M., Van der Weken, D., Kerre, E., Philips, W.: Soft Computing in Image Processing. Studies in Fuzziness and Soft Computing. Springer, Warsaw (2007)CrossRefzbMATHGoogle Scholar
  16. 16.
    Nokák, V., Perfilieva, I., Mockor, J.: Mathematical Principles of Fuzzy Logic. Springer Science and Business Media, NY (1999)zbMATHGoogle Scholar
  17. 17.
    Rodríguez, J.T., Guada, C., Gómez, D., Yáñez, J., Montero, J.: A methodology for hierarchical image segmentation evaluation. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Communications in Computer and Information Science, Part I, vol. 610, pp. 635–647. Springer, Eindhoven (2016)Google Scholar
  18. 18.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Champman and Hall Computing, Cambridge (1993)CrossRefGoogle Scholar
  19. 19.
    Wang, M.: Industrial Tomography. Systems and App. Woodhead Publishing, Cambridge (2015)Google Scholar
  20. 20.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of MathematicsComplutense UniversityMadridSpain
  2. 2.Faculty of StatisticsComplutense UniversityMadridSpain
  3. 3.Geosciences Institute (UCM-CSIC)Complutense UniversityMadridSpain

Personalised recommendations