Abstract
The task considered in this paper is performance evaluation of region segmentation algorithms in the ground truth (GT) based paradigm. Given a machine segmentation and a GT reference, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in computer vision. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.
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Ben-Hur, A., Elisseeff, A., Guyon, I.: A stability based method for discovering structure in clustered data. In: Proc. of Pacific Symposium on Biocomputing, pp. 6–17 (2002)
Chang, K.I., Bowyer, K.W., Sivagurunath, M.: Evaluation of texture segmentation algorithms. In: Proc. of CVPR, pp. 294–299 (1999)
Cingue, L., Cucciara, R., Levialdi, S., Martinez, S., Pignalberi, G.: Optimal range segmentation parameters through genetic algorithms. In: Proc. of 15th ICPR, Barcelona, vol. 1, pp. 474–477 (2000)
Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. Journal of the American Statistical Association 78, 553–569 (1983)
Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D., Bowyer, K., Eggert, D., Fitzgibbon, A., Fisher, R.: An experimental comparison of range image segmentation algorithms. IEEE Trans. on PAMI 18(7), 673–689 (1996)
Huang, Q., Dom, B.: Quantitative methods of evaluating image segmentation. In: Proc. of ICIP, pp. 53–56 (1995)
Jiang, X., Bowyer, K., Morioka, Y., Hiura, S., Sato, K., Inokuchi, S., Bock, M., Guerra, C., Loke, R.E., du Buf, J.M.H.: Some further results of experimental comparison of range image segmentation algorithms. In: Proc. of 15th ICPR, Barcelona, vol. 4, pp. 877–881 (2000)
Jiang, X.: An adaptive contour closure algorithm and its experimental evaluation. IEEE Trans. on PAMI 22(11), 1252–1265 (2000)
Jiang, X.: Performance evaluation of image segmentation algorithms. In: Chen, C.H., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, 3rd edn., pp. 525–542. World Scientific, Singapore (2005)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its applications to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. of ICCV, vol. 2, pp. 416–423 (2001)
Meila, M.: Comparing clusterings by the variation of information. In: Proc. of 6th Annual Conference on Learning Theory (2003)
Min, J., Powell, M., Bowyer, K.W.: Automated performance evaluation of range image segmentation algorithms. IEEE Trans. on SMC-B 34(1), 263–271 (2004)
Powell, M.W., Bowyer, K.W., Jiang, X., Bunke, H.: Comparing curved-surface range image segmenters. In: Proc. of 6th ICCV, Bombay, pp. 286–291 (1998)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66, 846–850 (1971)
Strehl, A., Gosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: Proc. of AAAI Workshop of Artificial Intelligence for Web Search, pp. 58–64 (2000)
van Dongen, S.: Performance criteria for graph clustering and Markov cluster experiments. Technical Report INS-R0012, Centrum voor Wiskunde en Informatica (2000)
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Jiang, X., Marti, C., Irniger, C., Bunke, H. (2005). Image Segmentation Evaluation by Techniques of Comparing Clusterings. In: Roli, F., Vitulano, S. (eds) Image Analysis and Processing – ICIAP 2005. ICIAP 2005. Lecture Notes in Computer Science, vol 3617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553595_42
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DOI: https://doi.org/10.1007/11553595_42
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