Decision fusion based unsupervised texture image segmentation

Authors:
Hua Zhong;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 2
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Design-based texture feature fusion using Gabor filters and co-occurrence probabilities

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Abstract

A decision fusion based method is proposed to improve unsupervised image segmentation. After the step of cluster label adjustment, each kind of texture is fixed with the same label. Then three simple fusion operators are applied according to the knowledge of multi-classifier fusion. Compared with feature fusion, decision fusion can combine the advantages of different features more intuitively and heuristically. Experimental results on textures and synthetic aperture radar (SAR) image demonstrate its superiority over feature fusion on removing the impact of noise feature and preserving the detail.