Abstract
We present a new unsupervised metric for segmentation result evaluation based on Bayes classification error and image global contrast. First, we presented a comparative study between several unsupervised metrics in order to prove their limits. The qualitative study was performed to make a preliminary selection and to discard some measures unsuitable for evaluation of foreground/background segmentation on flower images. For the quantitative study, we proposed a validation protocol based on the vote technique and involving a comparison to the ground truth. Experiments were performed on Oxford flower dataset in order to select the best result between different segmentation results. The obtained result showed that our proposed metric gives the best results.
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References
Liedtke, C.E., Gahm, T., Kappei, F.: Segmentation of microscopic cell scenes. Analytical and Quantitative Cytologie and Histology 9, 197–211 (1987)
Cho, K., Meer, P.: Image segmentation from consensus information. Computer Vision and Image Understanding 68, 72–89 (1997)
Voisine, N.: Approche adaptative de coopération hiérarchique de méthodes de segmentation: application aux images multicomposantes. PhD thesis. Université de Rennes I (2002)
Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognition 29, 1335–1346 (1996)
Chabrier, S., Laurent, H., Emile, B., Rosenburger, C., Marche, P.: A comparative study of supervised evaluation criteria for image segmentation. In: European Signal Processing Conference, pp. 1143–1146 (2004)
Yang, L., Albregtsen, L., Lonnestad, T., Grottum, P.: A supervised approach to the evaluation of image segmentation methods. In: Computer Analysis of Images and Patterns, pp. 759–765. Springer (1995)
Coquin, D., Bolon, P., Chehadeh, Y.: Evaluation quantitative d’images filtrées. In: GRETSI 1997, vol. 2, pp. 1351–1354 (1997)
Wilson, D.L., Baddeley, A.J., Owens, R.A.: A new metric for grey-scale image comparison. International Journal of Computer Vision 24, 5–17 (1997)
Zamperoni, P., Starovoitov, V.: On measures of dissimilarity between arbitrary gray-scale images. International Journal of Shape Modeling 2, 189–213 (1996)
Odet, C., Belaroussi, B., Cattin, H.: Scalable discrepancy measures for segmentation evaluation. In: ICIP 2002, pp. 785–788 (2002)
Pratt, W., Faugeras, O.D., Gagalowicz, A.: Visual discrimination of stochastic texture fields. IEEE Transactions on Systems, Man, and Cybernetics 8, 796–804 (1978)
Roman-Roldan, R., Gomez-Lopera, J.F., Atae-allah, C., Martinez-Aroza, J., Escamilla, P.L.L.: A measure of quality for evaluating methods of segmentation and edge detection. Pattern Recognition 34, 969–980 (2001)
Weszka, J.S., Rosenfeld, A.: Threshold evaluation techniques. IEEE Transactions Systems, Man, and Cybernetics 8, 622–629 (1978)
Levine, M.D., Nazif, A.M.: Dynamic measurement of computer generated image segmentations. IEEE Trans. Pattern Anal. Mach. Intell. 7, 155–164 (1985)
Sahoo, P.K., Soltani, S., Wong, A.K., Chen, Y.C.: A survey of thresholding techniques. Comput. Vision Graph. Image Process. 41, 233–260 (1988)
Cocquerez, J.P., Devars, J.: Détection de contours dans les images aériennes: Nouveaux opérateurs. Traitement du Signal 2, 45–65 (1985)
Demigny, D., Kamlé, T.: A discrete expression of canny’s criteria for step edge detector performances evaluation. EEE Transactions on Pattern Analysis and Machine Intelligence 19, 1199–1211 (1997)
Han, J.H., Kim, T.Y.: Ambiguity distance: an edge evaluation measure using fuzziness of edges. Fuzzy Sets and Systems, 311–324 (2002)
Tan, H., Gelfand, S., Delp, E.: A cost minimization approach to edge detection using simulated annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 3–18 (1992)
Pal, N.R., Pal, S.K.: Entropic thresholding. Signal Processing 16, 97–108 (1989)
Liu, J., Yang, Y.H.: Multiresolution color image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 689–700 (1994)
Zeboudj, R.: Filtrage, Seuillage Automatique, Contraste et Contours: du Pré-Traitement à l’Analyse d’image. PhD thesis. Université de Saint Etienne (1988)
Rosenberger, C., Chehdi, K.: Genetic fusion: application to multi-components image segmentation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 2223–2226 (2000)
Cochran, W.G., Snedecor, G.W.: Méthodes statistiques. Association de Coordination Technique, Agricole, Paris, France (1957)
Nilsback, M., Zisserman, A.: Delving deeper into the whorl of flower segmentation. IVC 28, 1049–1062 (2010)
Shufelt, J.A.: Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Trans. Pattern Anal. Mach. Intell. 21, 311–326 (1999)
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Najjar, A., Zagrouba, E. (2012). An Unsupervised Evaluation Measure of Image Segmentation: Application to Flower Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_44
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DOI: https://doi.org/10.1007/978-3-642-33191-6_44
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