JBC: joint boost clustering method for synthesis aperture radar images

Mengling Liu; Chu He; Gui-Song Xia; Xin Xu; Hong Sun

doi:10.1117/12.749062

15 November 2007 JBC: Joint Boost Clustering method for synthesis aperture radar images

Mengling Liu, Chu He, Gui-Song Xia, Xin Xu, Hong Sun

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 678814 (2007) https://doi.org/10.1117/12.749062
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Mengling Liu, Chu He, Gui-Song Xia, Xin Xu, and Hong Sun "JBC: Joint Boost Clustering method for synthesis aperture radar images", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678814 (15 November 2007); https://doi.org/10.1117/12.749062

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