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Proceedings Paper

JBC: joint boost clustering method for synthesis aperture radar images
Author(s): Mengling Liu; Chu He; Gui-Song Xia; Xin Xu; Hong Sun
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Paper Abstract

A clustering method based on Joint Boost for Synthesis Aperture Radar images is proposed. In this method, we follow the steps of Joint Boost, but substitute weak learns with basic clustering algorithm. We compute the sharing features between samples in order to reduce clustering times. The proposed clustering method, JBC constructs a new training set by random sampling from the original dataset, then selects the best feature and the best clusters for sharing, and calculates a distribution over the training samples using current shared feature and clusters, and finally a basic clustering algorithm (e.g. K-mean) is applied to partition the new training set. The final clustering solution is produced by aggregating the obtained partitions. The clustering results for SAR images show that the proposed method has a good performance.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678814 (15 November 2007); doi: 10.1117/12.749062
Show Author Affiliations
Mengling Liu, Wuhan Univ. (China)
Chu He, Wuhan Univ. (China)
Gui-Song Xia, Wuhan Univ. (China)
Xin Xu, Wuhan Univ. (China)
Hong Sun, 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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