Learning bayesian classifiers for scene classification with a visual grammar
Date
2005-03Source Title
IEEE Transactions on Geoscience and Remote Sensing
Print ISSN
0196-2892
Electronic ISSN
1558-0644
Publisher
IEEE
Volume
43
Issue
3
Pages
581 - 589
Language
English
Type
ArticleItem Usage Stats
182
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139
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Abstract
A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.
Keywords
Bayesian methodsLayout
Pixel
Image segmentation
Remote sensing
Image retrieval
Content based retrieval
Remote monitoring
Satellites
NASA