Evaluation of Segmentation Techniques Using Region Size and Boundary Information

  • D. P. Dogra
  • A. K. Majumdar
  • S. Sural
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

Image segmentation quality evaluation is a key element when comparing segmentation algorithms. In computer vision, unsupervised segmentation algorithms, although of great interest, often suffer from lack of a well-defined measure to evaluate. This paper presents a novel idea for evaluating such algorithms. A measure is proposed to evaluate four well referred segmentation algorithms. The metric proposed in this work is composed of both size and boundary of segments. When compared with some of the existing techniques, it is found that the proposed scheme can approximate the segmentation error in a better way.

Keywords

Segmentation Evaluation Area Matching Index Boundary Matching Index Combined Matching Index 

References

  1. 1.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From Contours to Regions: An Empirical Evaluation. In: CVPR (in press, 2009)Google Scholar
  2. 2.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. on PAMI 24(5), 603–619 (2002)Google Scholar
  3. 3.
    Comaniciu, D., Meer, P.: Mean Shift Image Segmentation Software, http://www.caip.rutgers.edu/riul/research/code/EDISON/index.html
  4. 4.
    Felzenszwalb, D.: Efficient Graph-based Image Segmentation. IJCV 59(2), 167–181 (2004)CrossRefGoogle Scholar
  5. 5.
    Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.: Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Huang, Q., Dom, B.: Quantitative Methods of Evaluating Image Segmentation. In: ICIP, pp. 53–56 (1995)Google Scholar
  7. 7.
    Kuan, Y., Kuo, C., Yang, N.: Color-Based Image Salient Region Segmentation Using Novel Region Merging Strategy. IEEE Trans. on MM 10(5), 832–845 (2008)CrossRefGoogle Scholar
  8. 8.
    Martin, D.: An Empirical Approach to Grouping and Segmentation. PhD Dissertation, Univ. of California, Berkeley (2002)Google Scholar
  9. 9.
    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. ICCV 2, 416–425 (2001)Google Scholar
  10. 10.
    Martin, D., Fowlkes, C., Malik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues. IEEE Trans. on PAMI 26(5), 530–549 (2004)Google Scholar
  11. 11.
    Pal, N.R., Pal, S.K.: A Review on Image Segmentation Techniques. Jour. of PR 26(9), 1277–1294 (1993)Google Scholar
  12. 12.
    Rand, W.: Objective Criteria for the Evaluation of Clustering Methods. Journal of ASA 66, 846–850 (1971)Google Scholar
  13. 13.
    Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Trans. on PAMI 22(8), 888–905 (2000)Google Scholar
  14. 14.
    Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward Objective Evaluation of Image Segmentation Algorithms. IEEE Trans. on PAMI 29(6), 929–944 (2007)Google Scholar
  15. 15.
    Weszka, J.S., Rosenfeld, A.: Threshold Evaluation Techniques. IEEE Trans. on SMC 8(3), 622–629 (1978)Google Scholar
  16. 16.
    Zhang, H., Frittb, J.E., Goldman, S.A.: Image Segmentation Evaluation: A Survey of Unsupervised Methods. Jour. of CVIU 110(2), 260–280 (2008)Google Scholar
  17. 17.
    Zhang, Y.J.: A Survey on Evaluation Methods for Image Segmentation. Jour. of PR 29(8), 1335–1346 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • D. P. Dogra
    • 1
  • A. K. Majumdar
    • 1
  • S. Sural
    • 2
  1. 1.Department of Computer Sc. & EngineeringIndian Institute of TechnologyKharagpurIndia
  2. 2.School of Information TechnologyIndian Institute of TechnologyKharagpurIndia

Personalised recommendations