Abstract
Inspired by the idea of multi-view, we proposed an image segmentation algorithm using co-EM strategy in this paper. Image data are modeled using Gaussian Mixture Model (GMM), and two sets of features, i.e. two views, are employed using co-EM strategy instead of conventional single view based EM to estimate the parameters of GMM. Compared with the single view based GMM-EM methods, there are several advantages with the proposed segmentation method using co-EM strategy. First, imperfectness of single view can be compensated by the other view in the co-EM. Second, employing two views, co-EM strategy can offer more reliability to the segmentation results. Third, the drawback of local optimality for single view based EM can be overcome to some extent. Fourth, the convergence rate is improved. The average time is far less than single view based methods. We test the proposed method on large number of images with no specified contents. The experimental results verify the above advantages, and outperform the single view based GMM-EM segmentation methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Sonka, M., Hilavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 2nd edn. Brooks/Cole (1998)
Ma, W., Manjunath, B.S.: EdgeFlow: A technique for boundary detection and image segmentation. IEEE Trans. Image Process 9(8), 1375–1388 (2000)
Belongie, S., Carson, C., Greenspan, H., Malik, J.: Color- and texture-based image segmentation using EM and its application to content-based image retrieval. In: IEEE Proc. Int. Conf. Computer Vision, pp. 675–682. IEEE Computer Society Press, Los Alamitos (1998)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using Expectation-Maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002)
Muslea, I., Mintion, S., Knoblock, C.A.: Active + semi-supervised learning = robust multi-view learning. In: Proc. Int. Conf. Machine Learning, pp. 435–442 (2002)
Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proc. Intl Conf. of Information and Knowledge Management, pp. 86–93 (2000)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Annual Workshop on Computational Learning Theory, pp. 92–100 (1998)
Yi, X., Zhang, C., Wang, J.: Multi-view EM algorithm and its application to color image segmentation. In: IEEE Proc. Int. Conf. Multimedia and Expo, pp. 351–354. IEEE Computer Society Press, Los Alamitos (2004)
Muslea, I., Minton, S.N., Knoblock, C.A.: Active learning with strong and weak views: A case study on wrapper induction. In: Proc. Int. Joint Conf. on Artificial Intelligence, pp. 415–420 (2003)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)
Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of Optical Society of America A 2(7) (1985)
Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Z., Cheng, J., Liu, Q., Lu, H. (2007). Image Segmentation Using Co-EM Strategy. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_81
Download citation
DOI: https://doi.org/10.1007/978-3-540-76390-1_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76389-5
Online ISBN: 978-3-540-76390-1
eBook Packages: Computer ScienceComputer Science (R0)