Abstract
In spite of significant advances in image segmentation techniques, evaluation of these methods thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images that are evaluated by some method, or it is otherwise left to subjective evaluation by the reader. We propose a new approach for evaluation of segmentation that takes into account not only the accuracy of the boundary localization of the created segments but also the under-segmentation and over-segmentation effects, regardless to the number of regions in each partition. In addition, it takes into account the way humans perceive visual information. This new metric can be applied both to automatically provide a ranking among different segmentation algorithms and to find an optimal set of input parameters of a given algorithm.
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References
Cardoso, J.S., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Transactions on Image Processing 14(11), 1773–1782 (2005)
Gelasca, E.D., Ebrahimi, T., Farias, M.C.Q., Carli, M., Mitra, S.K.: Towards perceptually driven segmentation evaluation metrics. In: Proc. IEEE Computer Vision and Pattern Recognition Workshop, vol. 4, p. 52 (2004)
Huang, Q., Dom, Byron: Quantitative methods of evaluating image segmentation. In: Proc. IEEE International Conference on Image Processing, vol. III, pp. 53–56 (1995)
Levine, M.D., Nazif, A.M.: Dynamic measurement of computer generated image segmentations. Trans. Pattern Analysis and Machine Intelligence 7(2), 155–164 (1985)
Martin, D., Fowlkes, C.: The Berkeley segmentation database and benchmark, online at, http://www.cs.berkeley.edu/projects/vision/grouping/segbench/
Martin, D.: An empirical approach to grouping and segmentation, Ph.D dissertation, University of California, Berkeley (2002)
Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: Still image objective segmentation evaluation using ground truth. In: Proc. of 5th COST 276 Workshop, pp. 9–14 (2003)
Odet, C., Belaroussi, B., Cattin, H.B.: Scalable discrepancy measures for segmentation evaluation. In: Proc. Intern. Conf. on Image Processing, vol. I, pp. 785–788 (2002)
Peleg, S., Werman, M., Rom, H.: A unified approach to the change of resolution: Space and gray-level. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 739–742 (1989)
Raghavan, V., Bollmann, P., Jung, G.: A critical investigation of recall and precision as measures of retrieval system performance. ACM Transactions on Information Systems 7(3), 205–229 (1989)
Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
Sahoo, P.K., Soltani, S., Wang, A.K.C.: A survey of thresholding techniques. Computer Vision, Graphics and Image Processing 41(2), 233–260 (1988)
Yasnoff, W.A., Mui, J.K., Bacus, J.W.: Error measures in scene segmentation. Pattern Recognition 9(4), 217–231 (1977)
Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognition 29(8), 1335–1346 (1996)
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Monteiro, F.C., Campilho, A.C. (2006). Performance Evaluation of Image Segmentation. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_24
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DOI: https://doi.org/10.1007/11867586_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44891-4
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