Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers

  • Daesik Jang
  • Gregor Miller
  • Sidney Fels
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)


The majority of vision research focusses on advancing technical methods for image analysis, with a coupled increase in complexity and sophistication. The problem of providing access to these sophisticated techniques is largely ignored, leading to a lack of application by mainstream applications. We present a feature-based clustering segmentation algorithm with novel modifications to fit a developer-centred abstraction. This abstraction acts as an interface which accepts a description of segmentation in terms of properties (colour, intensity, texture, etc.), constraints (size, quantity) and priorities (biasing a segmentation). This paper discusses the modifications needed to fit the algorithm into the abstraction, which conditions of the abstraction it supports, and results of the various conditions demonstrating the coverage of the segmentation problem space. The algorithm modification process is discussed generally to help other researchers mould their algorithms to similar abstractions.


Feature Space Image Segmentation IEEE Computer Society Cluster Node Colour Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shreiner, D., Woo, M., Neider, J., Davis, T.: OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2, 5th edn. Addison-Wesley Professional (2005)Google Scholar
  2. 2.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, 1st edn. O’Reilly Media, Inc. (2008)Google Scholar
  3. 3.
    Shaw, K.B., Lohrenz, M.C.: A survey of digital image segmentation algorithms. Final Technical Report ADA499374, Naval Oceanographic and Atmospheric Research Lab (1995)Google Scholar
  4. 4.
    Skarbek, W., Koschan, A.: Colour image segmentation - a survey. Technical report, Institute for Technical Informatics, Technical University of Berlin (1994)Google Scholar
  5. 5.
    Chan, T., Sandberg, B., Moelich, M.: Some recent developments in variational image segmentation. In: Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse Problems, pp. 175–210. Springer (2005)Google Scholar
  6. 6.
    Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding 110, 260–280 (2008)CrossRefGoogle Scholar
  7. 7.
    Raut, S., Raghuvanshi, M., Dharaskar, R., Raut, A.: Image segmentation: A state-of-art survey for prediction. In: Proceedings of International Conference on Advanced Computer Control, pp. 420–424. IEEE Computer Society, New York (2009)Google Scholar
  8. 8.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar
  9. 9.
    Lucchese, L., Mitra, S.K.: Advances in color image segmentation. In: Proceedings of Global Telecommunications Conference, pp. 2038–2044. IEEE Computer Society, Berkeley (1999)Google Scholar
  10. 10.
    Bow, S.T.: Pattern Recognition and Image Preprocessing, 2nd edn. CRC Press (2002)Google Scholar
  11. 11.
    Pavlidis, T., Liow, Y.T.: Integrating region growing and edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 225–233 (1990)CrossRefGoogle Scholar
  12. 12.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  13. 13.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  14. 14.
    Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae-Special issue on mathematical morphology 41, 187–228 (2000)MathSciNetzbMATHGoogle Scholar
  15. 15.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  16. 16.
    Eumt, K.-B., Lee, J., Venetsanopoulos, A.N.: Color image segmentation using a possibilistic approach. In: IEEE International Conference on Systems, Man, and Cybernetics - SMC, vol. 2, pp. 1150–1155. IEEE Computer Society, New York (1996)Google Scholar
  17. 17.
    Comaniciu, D., Meer, P.: Robust analysis of feature spaces: Color image segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 750–755. IEEE Computer Society, New York (1997)CrossRefGoogle Scholar
  18. 18.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  19. 19.
    Wang, W.: Color image segmentation and understanding through connected components. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 1089–1093. IEEE Computer Society, New York (1997)Google Scholar
  20. 20.
    Samet, H., Tamminen, M.: Efficient component labeling of images of arbitrary dimension represented by linear bintrees. Transactions on Pattern Analysis and Machine Intelligence 10, 579–586 (1988)CrossRefGoogle Scholar
  21. 21.
    Cardoso, J., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Transactions on Image Processing 14, 1773–1782 (2005)CrossRefGoogle Scholar
  22. 22.
    Frucci, M., Perner, P., Sanniti di Baja, G.: Case-based-reasoning for image segmentation. Pattern Recognition and Artificial Intelligence 22, 829–842 (2008)CrossRefGoogle Scholar
  23. 23.
    Yong, X., Feng, D., Rongchun, Z., Petrou, M.: Learning-based algorithm selection for image segmentation. Pattern Recognition Letters 26, 1059–1068 (2005)CrossRefGoogle Scholar
  24. 24.
    Martin, V., Maillot, N., Thonnat, M.: A learning approach for adaptive image segmentation. In: Proceedings of the Fourth IEEE International Conference on Computer Vision Systems (ICVS 2006). IEEE Computer Society (2006)Google Scholar
  25. 25.
    Nickisch, H., Kohli, P., Rother, C., Rhemann, C.: Learning an interactive segmentation system. In: Proceedings of the 7th Indian Conference on Computer Vision, Graphics and Image Processing, pp. 274–281. ACM, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daesik Jang
    • 1
  • Gregor Miller
    • 2
  • Sidney Fels
    • 2
  1. 1.Kunsan National UniversityGunsanSouth Korea
  2. 2.Human Communication Technologies LaboratoryUniversity of British ColumbiaVancouverCanada

Personalised recommendations