Abstract An adaptive neighborhood approach is proposed. The Markov random fields (MRF) segmentation approach with adaptive neighborhood systems is utilized to preserve detail features and border areas and to improve the segmentation effect. Bayesian inference is applied to integrate the different information sources of local image around the pixels. To improve the reliability of the belief value and the adaptivity, fuzzy c-means (FCM) clustering is introduced in Bayesian network. Thus, the selection of the neighborhood in the region label process need not depend on the known priori knowledge by applying the FCM. The neighborhood with the highest belief value in the threshold scope is chosen to compute the MRF region label process. Experimental results demonstrate that the segmentation effect of the proposed algorithm is superior to that of the classical MRF and hidden Markov random field with detail structures well preserved.
[1] Stan Z L. Markov Random Field Modeling in Image Analysis. 2nd Edition. Berlin, Germany: Springer-Verlag, 2000 [2] Cross G R, Jain A. Markov Random Field Texture Models. IEEE Trans on Pattern Analysis and Machine Intelligence, 1983, 5(1): 25-39 [3] Deng Huawu, Clausi D A. Unsupervised Segmentation of Synthetic Aperture Radar Sea Ice Imagery Using a Novel Markov Random Field Model. IEEE Trans on Geoscience and Remote Sensing, 2005, 43(3): 528-538 [4] Wainwright M J, Jordan M I. Log-Determinant Relaxation for Approximate Inference in Discrete Markov Random Fields. IEEE Trans on Signal Processing, 2006, 56(4): 2099-2109 [5] Destrempes F, Angers J F, Mignotte M. Fusion of Hidden Markov Random Field Models and Its Bayesian Estimation. IEEE Trans on Image Processing, 2006, 15(10): 2920-2935 [6] Gu D B, Sun J X. EM Image Segmentation Algorithm Based on an Inhomogeneous Hidden MRF Model. IEE Proceedings—Vision, Image and Signal Processing, 2005, 152(2): 184-190 [7] Bovolo F, Bruzzone L. A Detail-Preserving Scale-Driven Approach to Change Detection in Multitemporal SAR Images. IEEE Trans on Geoscience and Remote Sensing, 2005, 43(12): 2963-2972 [8] Smits P C, Dellepiane S G. Synthetic Aperture Radar Image Segmentation by a Detail Preserving Markov Random Field Approach. IEEE Trans on Geoscience and Remote Sensing, 1997, 35(4): 844-857 [9] Haralick R M, Shapiro L G. Computer and Robot Vision. New York, USA: Addison Wesley, 1992 [10] Duda R O, Hart P E, Stork D G. Pattern Classification. New York, USA: John Wiley and Sons, 2001 [11] Besag J. On the Statistical Analysis of Dirty Pictures. Journal of the Royal Statistical Society: Series B, 1986, 48(3): 259-302 [12] Rignot E, Chellappa R. Segmentation of Polarimetric Synthetic Aperture Radar Data. IEEE Trans on Image Processing, 1992, 1(3): 281-300
2008, Vol. 21 
