Abstract / Description of output
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.
Original language | English |
---|---|
Title of host publication | Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 393-401 |
Number of pages | 9 |
ISBN (Print) | 0-7803-5060-X |
DOIs | |
Publication status | Published - 1 Aug 1998 |
Keywords / Materials (for Non-textual outputs)
- directed graphs
- image classification
- image segmentation
- neural nets
- prediction theory
- trees (mathematics)
- belief networks
- class labels
- local predictions
- scaled-likelihood method
- training images
- Artificial neural networks
- Computer science
- Fuses
- Hidden Markov models
- Image segmentation
- Neural networks
- Pattern classification
- Pixel
- Predictive models
- System testing