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An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model
January 1996 (vol. 18 no. 1)
pp. 69-73

Abstract—This paper presents a unified approach for the image understanding problem based on the MRF models. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process so that the erroneous segmentation and misinterpretation can be compensately recovered by continuous estimation of the unified energy function.

[1] Y. Yakimovsky and J.A.I Feldman, "A semantics-based decision theory region analyzer," Proc. IJCAI, vol. 3, pp. 580-588, 1973.
[2] J.M. Tenenbaum and H.G. Barrow, "Experiments in interpretation-guided segmentation," Artificial Intelligence, vol. 8, pp. 241-274, 1977.
[3] Y. Ohta, Knowledge-Based Interpretation of Outdoor Natural ColorScenes.Massachusetts: Pitman Publishing, 1985.
[4] S. Geman and D. Geman, "Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, 1984.
[5] E.B. Gamble, D. Geiger, and T. Poggio, "Integration of vision modules and labeling of surface discontinuities," IEEE Trans. Systems, Man, and Cybernetics, vol. 19, pp. 1,576-1,581, 1989.
[6] J.W. Modestino and J. Zhang, "A Markov random field model-based approach to image interpretation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, pp. 606-615, 1992.
[7] M.S. Suk and S.M. Jung, "A new image segmentation technique based on partition mode test," Pattern Recognition, vol. 16, pp. 469-480, 1983.
[8] I.Y. Kim and H.S. Yang, "A systematic way for region-based image segmentation based on Markov random field model," Pattern Recognition Letters, vol. 15, pp. 969-976, 1994.
[9] I.Y. Kim and H.S. Yang, "Efficient image labeling based on Markov random field and error backpropagation network," Pattern Recognition, vol. 26, pp. 1,695-1,707, 1993.
[10] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, pp. 671-680, 1983.

Index Terms:
Region clustering, region labeling, Markov random field, energy function, optimization.
Citation:
Ii Y. Kim, Hyun S. Yang, "An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 1, pp. 69-73, Jan. 1996, doi:10.1109/34.476014
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