Training Bayesian networks for image segmentation

Xiaojuan Feng; Christopher K. I. Williams

doi:10.1117/12.323459

24 September 1998 Training Bayesian networks for image segmentation

Xiaojuan Feng, Christopher K. I. Williams

Proceedings Volume 3457, Mathematical Modeling and Estimation Techniques in Computer Vision; (1998) https://doi.org/10.1117/12.323459
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) conditional maximum likelihood (CML). Segmentation obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor- scene segmentation task.

Citation Download Citation

Xiaojuan Feng and Christopher K. I. Williams "Training Bayesian networks for image segmentation", Proc. SPIE 3457, Mathematical Modeling and Estimation Techniques in Computer Vision, (24 September 1998); https://doi.org/10.1117/12.323459

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