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Proceedings Paper

Texture classification of aerial image based on Bayesian networks
Author(s): Li Ma; Hongjing Yu; Jiatian Li; Hao Chen
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Paper Abstract

Classification is a basic topic in data mining and pattern recognition. Following advances in computer science, a lot of new methods have been proposed in recent years, such as artificial neural networks, decision trees, fuzzy set and Bayesian Networks, etc. As a probabilistic network, Bayesian Networks is a powerful tool for handling uncertainty in data mining and many other domains. Naïve Bayes Classifier (NBC) is a simple and effective classification method, which is built on the assumption of conditional independence between the class attributes. This topology structure can not describe the inherent relation among the features. In this paper, we apply Bayesian Networks Augmented Naïve Bayes (BAN) for the texture classification of aerial images, which relaxes the independent assumption in NBC. A new method for learning the networks topology structure based on training samples is adopted in this paper. Comparison experiments show higher accuracy of BAN classifier than NBC. The results also show the potential applicability of the proposed method.

Paper Details

Date Published: 15 November 2007
PDF: 6 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880H (15 November 2007); doi: 10.1117/12.746321
Show Author Affiliations
Li Ma, Wuhan Univ. (China)
Hongjing Yu, China Institute of Water Resources and Hydropower Research (China)
Jiatian Li, China Univ. of Mining and Technology (China)
Hao Chen, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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