Image segmentation via multi-scaled belief propagation

Image segmentation plays an important role in computer vision and image analysis. In this paper, we develop a novel algorithm which can automatically segment an image into regions with relative uniform texture or color without the need to decide the region number in advance. In this work, the segmentation is formulated as a labeling problem in the Markov random fields (MRFs) model. An efficient multi-scale belief propagation (BP) algorithm is used to find the solution to the MRF estimation. Extensive experiments have shown the effectiveness of our approach.

[1]  Zhuowen Tu,et al.  An integrated framework for image segmentation and perceptual grouping , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[2]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Shimon Ullman,et al.  Learning to Segment , 2004, ECCV.

[5]  David G. Stork,et al.  Pattern Classification , 1973 .

[6]  Wolfgang Förstner,et al.  A Framework for Low Level Feature Extraction , 1994, ECCV.

[7]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Andrew Zisserman,et al.  A Boundary-Fragment-Model for Object Detection , 2006, ECCV.

[9]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Andrew Zisserman,et al.  OBJ CUT , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[12]  Thrasyvoulos N. Pappas,et al.  An Adaptive Clustering Algorithm For Image Segmentation , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[13]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[14]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[15]  Andrew Zisserman,et al.  Incremental learning of object detectors using a visual shape alphabet , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[16]  William T. Freeman,et al.  On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs , 2001, IEEE Trans. Inf. Theory.

[17]  Jianbo Shi,et al.  Multiclass spectral clustering , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Tony Lindeberg,et al.  Direct computation of shape cues using scale-adapted spatial derivative operators , 1996, International Journal of Computer Vision.

[20]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields , 2006, ECCV.

[21]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[22]  Daniel P. Huttenlocher,et al.  Efficient Belief Propagation for Early Vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[23]  Harry Shum,et al.  Image segmentation by data driven Markov chain Monte Carlo , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[24]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[25]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..