Abstract
This paper presents a novel alternating scheme for supervised parameter learning. While in previous methods parameters were optimized simultaneously, we propose to optimize parameters in an alternating way. In doing so the computational amount is reduced significantly. The method is applied to four image segmentation algorithms and compared with exhaustive search and a coarse-to-fine approach. The results show the efficiency of the proposed scheme.
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
Christoudias, C.M., Georgescu, B., Meer, P., Georgescu, C.M.: Synergism in low level vision. In: Proc. of Int. Conf. on Pattern Recognition, pp. 150–155 (2002)
Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23, 800–810 (2001)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Computer Vision 59, 167–181 (2004)
Jiang, X.: An adaptive contour closure algorithm and its experimental evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1252–1265 (2000)
Jiang, X., Irniger, C., Bunke, H.: Training/test data partitioning for empirical performance evaluation. In: Christensen, H.I., Phillips, P.J. (eds.) Empirical Evaluation Methods in Computer Vision, pp. 23–37. World Scientific, Singapore (2002)
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)
Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26, 530–539 (2004)
Min, J., Powell, M., Bowyer, K.W.: Automated performance evaluation of range image segmentation algorithms. IEEE Trans. Systems Man and Cybernetics - Part B: Cybernetics 34, 263–271 (2004)
Rehrmann, V., Priese, L.: Fast and robust segmentation of natural color scenes. In: Proc. of Third Asian Conf. on Computer Vision, vol. 1, pp. 598–606 (1997)
Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. on Machine Learning Research 3, 583–617 (2002)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29, 929–944 (2007)
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). Alternating Scheme for Supervised Parameter Learning with Application to Image Segmentation. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-23672-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23671-6
Online ISBN: 978-3-642-23672-3
eBook Packages: Computer ScienceComputer Science (R0)