An Evaluation Measure of Image Segmentation Based on Object Centres

  • J. J. Charles
  • L. I. Kuncheva
  • B. Wells
  • I. S. Lim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)


Classification of organic materials obtained from rock and drill cuttings involves finding multiple objects in the image. This task is usually approached by segmentation. The quality of segmentation is evaluated by matching the whole detected objects to a reference segmentation. We are interested in representing each object by a single reference point called the “centre”. This paper proposes an evaluation measure of image segmentation for such representation. We argue that measures based only on distance between obtained centres and a set of predefined centres are insufficient. The proposed measure is based on a list of desirable properties of the segmentation. The three components of the measure evaluate the under/over segmentation of the objects, the proportion of centres placed in the background rather than in objects, and the distance between the guessed and the true centres. The ability of the measure to distinguish between segmentation results of different quality is illustrated on three sets of examples including an image containing microfossils and pieces of inert material.


Image segmentation evaluation measures discrepancy methods microfossils palynomorphs 


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  1. 1.
    Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognition 29(8), 1335–1346 (1996)CrossRefGoogle Scholar
  2. 2.
    Beauchemin, M., Thomson, K.P.B.: The evaluation of segmentation results and the overlapping area matrix. Remote Sensing 18(18), 3895–3899 (1997)CrossRefGoogle Scholar
  3. 3.
    Cardoso, J.S., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Transactions on Image Processing 14(10), 1773–1782 (2005)CrossRefGoogle Scholar
  4. 4.
    Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P., Bunke, H., Goldgof, D., Bower, K., Eggert, D., Filtzbiggbon, A., Fisher, R.: An eperimental comparison of range image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 673–689 (1996)CrossRefGoogle Scholar
  5. 5.
    Borsotti, M., Campadelli, P., Schettini, R.: Quantitative evaluation of color image segmentation results. Pattern Recognition Letters 19, 741–747 (1998)CrossRefzbMATHGoogle Scholar
  6. 6.
    Liu, J., Yang, Y.H.: Multiresolution color image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(7), 689–700 (1994)CrossRefGoogle Scholar
  7. 7.
    Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66, 846–850 (1971)CrossRefGoogle Scholar
  8. 8.
    Elisseeff, A.B.H.A., Guyon, I.: A stability based method for discovering structure in clustered data. In: Proc. Pacific Symposium on Biocomputing, pp. 6–17 (2002)Google Scholar
  9. 9.
    Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2, 193–218 (1985)CrossRefGoogle Scholar
  10. 10.
    Bollmann, J., Quinn, P.S., Vela, M., Brabec, B., Brechner, S., Cortes, M.Y., Hilbrecht, H., Schmidt, D.N., Schiebel, R., Thierstein, H.R.: Automated particle analysis: Calcareous microfossils. Image Analysis, Sediments and Paleoenvironments 7, 229–252 (2004)CrossRefGoogle Scholar
  11. 11.
    Charles, J.J., Kuncheva, L.I., Wells, B., Lim, I.S.: Object location within microscopic images of palynofacies (submitted, 2006)Google Scholar
  12. 12.
    Borgefors, G.: Distance transformations in digital images. Computer Vision Graphics and Image Processing 34(3), 344–371 (1986)CrossRefGoogle Scholar
  13. 13.
    Wang, Y., Chou, J.: Automatic segmentation of touching rice kernels with an active contour model. Transactions of the ASAE 47(5), 1803–1811 (2004)Google Scholar
  14. 14.
    Paul, G.V., Beach, G.J., Cohen, C.J.: A realtime object tracking system using a color camera. In: Applied Imagery Pattern Recognition Workshop, pp. 137–142 (2001)Google Scholar
  15. 15.
    Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. J. Charles
    • 1
  • L. I. Kuncheva
    • 1
  • B. Wells
    • 2
  • I. S. Lim
    • 1
  1. 1.School of InformaticsUniversity of WalesBangorUnited Kingdom
  2. 2.Conwy Valley Systems Ltd.United Kingdom

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