Abstract
In this paper, we propose an unsupervised color image segmentation scheme using homotopy continuation method and Compound Markov Random Field (CMRF) model. The proposed scheme is recursive in nature where model parameter estimation and the image label estimation are alternated. Ohta (I 1, I 2, I 3) model is used as the color model for image segmentation and we propose a compound MRF model taking care of intra-color and inter-color plane interactions. The CMRF model parameters are estimated using Maximum Conditional Pseudo Likelihood (MCPL) criterion and the MCPL estimates are obtained using homotopy continuation method. The image label estimation is formulated using Maximum a Posteriori criterion and the MAP estimates are obtained using hybrid algorithm. In the context of misclassification error, the proposed unsupervised scheme with CMRF model exhibited improved segmentation accuracy as compared to MRF model and Kato’s method.
Chapter PDF
Similar content being viewed by others
References
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color Image Segmentation: Advances and prospects. Pattern Recog. 34, 2259–2281 (2001)
Ohta, Y.I., Kanade, T., Sakai, T.: Color information for region segmentation. Comp. Grap. Image. Process. 62, 222–241 (1980)
Geman, S., Geman, D.: Stochastic relaxation,Gibbs distributions and the Bayesian restoration of images. IEEE Trans. on PAMI 6, 721–741 (1984)
Zhang, J., Modestino, J.W.: A Model-Fitting Approach to Cluster Validation with Application to Stochastic Model-based Image Segmentation. IEEE Trans. on PAMI 12(10), 1009–1017 (1990)
Kato, Z., Pong, T.C., Lee, J.C.M.: Color image segmentation and parameter estimation in a markovian framework. Pattern Recognition Letters 22, 309–321 (2001)
Chow, N., Mallet-Paret, J., Yorke, J.A.: Finding zeros of maps: homotopy methods that are constructive with probability one. Math. Computation 32(143), 887–899 (1978)
Stonick, V.L., Alexander, S.T.: A Relationship between recursive least square update and homotopy continuation methods. IEEE Trans. Signal Processing 39(2), 530–532 (1991)
Wendell, R.E., Horter Jr., A.P.: Minimization of a non-separable objective function subject to disjoint constraints. Operations Research 24(4), 643–657 (1976)
Sucheta, P., Nanda, P.K.: Color Image using Constrained Compound Markov Random Field Model and Homotopy Continuation Method. In: Proc. of the first International Conference on Distributed Frameworks and Applications, Universiti Sains Malaysia, Penang, Malaysia, pp. 151–158 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Panda, S., Nanda, P.K. (2009). Unsupervised Color Image Segmentation Using Compound Markov Random Field Model. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-11164-8_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11163-1
Online ISBN: 978-3-642-11164-8
eBook Packages: Computer ScienceComputer Science (R0)