Combining neural networks and belief networks for image segmentation

被引：0

作者：

Williams, CKI ^{[1
]}

Feng, XJ ^{[1
]}

机构：

[1] Univ Edinburgh, Dept Artificial Intelligence, Edinburgh EH1 2QL, Midlothian, Scotland

来源：

NEURAL NETWORKS FOR SIGNAL PROCESSING VIII | 1998年

关键词：

D O I：

10.1109/NNSP.1998.710669

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we are concerned with segmenting an image into a number of predefined classes. We show how to fuse together local predictions for the class labels with a prior model of segmentations using the scaled-likelihood method. The prior model is based on a tree-structured belief network. Both the neural network and belief network were trained on a set of training images, and then the combined system was used to make predictions on a set of test images. We show that the combined neural network/belief network classifier gives improved prediction accuracy on 9 out of the 11 classes.

引用

页码：393 / 401

页数：9

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