Abstract
This paper addresses the parameter selection problem in image segmentation. Mostly, segmentation algorithms have parameters which are usually fixed beforehand by the user. Typically, however, each image has its own optimal set of parameters and in general a fixed parameter setting may result in unsatisfactory segmentations. In this paper we present a novel unsupervised framework for automatically choosing parameters based on a comparison with the results from some reference segmentation algorithm(s). The experimental results show that our framework is even superior to supervised selection method based on ground truth. The proposed framework is not bounded to image segmentation and can be potentially applied to solve the adaptive parameter selection problem in other contexts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Min, J., Powell, M.W., Bowyer, K.W.: Automated performance evaluation of range image segmentation algorithms. IEEE Trans. on Systems, Man, and Cybernetics, Part B 34, 263–271 (2004)
Chabrier, S., Emile, B., Rosenberger, C., Laurent, H.: Unsupervised performance evaluation of image segmentation. EURASIP J. Appl. Signal Process. (2006)
Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding 110, 260–280 (2008)
Pignalberi, G., Cucchiara, R., Cinque, L., Levialdi, S.: Tuning range image segmentation by genetic algorithm. EURASIP J. Appl. Signal Process., 780–790 (2003)
Abdul-Karim, M.A., Roysam, B., Dowell-Mesfin, N., Jeromin, A., Yuksel, M., Kalyanaraman, S.: Automatic selection of parameters for vessel/neurite segmentation algorithms. IEEE Trans. on Image Processing 14, 1338–1350 (2005)
Wattuya, P., Jiang, X.: Ensemble combination for solving the parameter selection problem in image segmentation. In: Proc. of Int. Conf. on Pattern Recognition, pp. 392–401 (2008)
Rabinovich, A., Lange, T., Buhmann, J., Belongie, S.: Model order selection and cue combination for image segmentation. In: CVPR, pp. 1130–1137 (2006)
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. In: Proc. ICCV, vol. 2, pp. 416–423 (2001)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Computer Vision 59, 167–181 (2004)
Arbelaez, P., Maire, M., Fowlkes, C.C., Malik, J.: From contours to regions: An empirical evaluation. In: CVPR, pp. 2294–2301. IEEE, Los Alamitos (2009)
Rao, S., Mobahi, H., Yang, A.Y., Sastry, S., Ma, Y.: Natural image segmentation with adaptive texture and boundary encoding. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5994, pp. 135–146. Springer, Heidelberg (2010)
Christoudias, C.M., Georgescu, B., Meer, P., Georgescu, C.M.: Synergism in low level vision. In: Int. Conf. on Pattern Recognition, pp. 150–155 (2002)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence 29, 929–944 (2007)
Monteiro, F.C., Campilho, A.C.: Performance evaluation of image segmentation. In: Int. Conf. on Image Analysis and Recognition, vol. (1), pp. 248–259 (2006)
Fowlkes, C., Martin, D., Malik, J.: Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches. In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 54–61 (2003)
Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 530–539 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Franek, L., Jiang, X. (2011). Adaptive Parameter Selection for Image Segmentation Based on Similarity Estimation of Multiple Segmenters. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-19309-5_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19308-8
Online ISBN: 978-3-642-19309-5
eBook Packages: Computer ScienceComputer Science (R0)