Training Bayesian networks for image segmentation


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) emphconditional maximum likelihood (CML). Segmentations 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.

Divisions: Aston University (General)
Additional Information: Vol. 3457 DOI 10.1117/12.323459 Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Event Title: Proceedings of SPIE vol 3457
Event Type: Other
Event Dates: 1998-09-24 - 1998-09-24
Uncontrolled Keywords: Bayesian image analysis,image segmentation,Random Fields,quadtree model,Bouman and Shapiro,hierarchical model,conditional-probability tables,synthetic images,real-world,outdoor-scene,segmentation task
Last Modified: 29 May 2024 07:25
Date Deposited: 16 Sep 2009 14:58
PURE Output Type: Paper
Published Date: 1998-09-24
Authors: Feng, Xiaojuan
Williams, Christopher K. I.



Version: Accepted Version

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