Abstract
In this paper, we present a color image segmentation algorithm based on a finite mixture model and examine its application to natural scene segmentation. Gaussian mixture model (GMM) is first adopted to represent the statistical distribution of multi-colored objects. Then a deterministic annealing Expectation Maximization (DAEM) formula is used to estimate the parameters of the GMM. The experimental results show that the proposed DAEM can avoid the initialization problem unlike the standard EM algorithm during the maximum likelihood (ML) parameter estimation and natural scenes containing texts are segmented more efficiently than the existing EM technique.
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
McLachlan, G.J., Nguyen, S.K., Galloway, G.J., Wang, D.: Clustering of magnetic resonance images. Technical Report, Department of Mathematics, University of Queensland (1998)
Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)
Permuter, H., Francos, J., Jermyn, I.H.: Gaussian mixture models of texture and colour for image database retrieval. In: Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. III, pp. 569–572 (2003)
Chen, X., Yang, J., Zhang, J., Waibel, A.: Automatic detection and recognition of signs from natural scenes. IEEE Transactions on Image Processing 13(1), 87–99 (2004)
Ueda, N., Nakano, R.: Deterministic annealing EM algorithm. Neural Networks 11, 271–282 (1998)
Hofmann, T., Buhman, J.M.: Pairwise data clustering by deterministic annealing. IEEE Transactions on PAMI 19(1), 1–13 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, J., Cho, W., Park, S. (2004). Deterministic Annealing EM and Its Application in Natural Image Segmentation. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_100
Download citation
DOI: https://doi.org/10.1007/978-3-540-30497-5_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24127-0
Online ISBN: 978-3-540-30497-5
eBook Packages: Computer ScienceComputer Science (R0)