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Proceedings Paper

Path-based similarity with instance-level constraints for SemiBoost
Author(s): Xiangrong Zhang; Jianshen Yu; Ting Wang; Biao Hou; L. C. Jiao
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Paper Abstract

In this paper, a novel classification method path-based similarity with instance-level constrains for SemiBoost, PBS-SB in short is proposed, and we exploit it for synthetic aperture radar automatic target recognition (SAR-ATR). Different from traditional SemiBoost method that uses the Gaussian kernel similarity, PBS-SB utilizes the path-based similarity, which considers the global consistence of data clusters. Besides, the instance-level constraints are integrated into the similarity measurement to construct the semi-supervised similarity, which provides the local consistence information. The experiments on 5 different data sets and MSTAR (Moving and Stationary Target Acquisition and Recognition) database demonstrate that the proposed method has superior classification performance with respect to competitive methods.

Paper Details

Date Published: 27 October 2013
PDF: 8 pages
Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891911 (27 October 2013); doi: 10.1117/12.2031773
Show Author Affiliations
Xiangrong Zhang, Xidian Univ. (China)
Jianshen Yu, Xidian Univ. (China)
Ting Wang, Xidian Univ. (China)
Biao Hou, Xidian Univ. (China)
L. C. Jiao, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 8919:
MIPPR 2013: Pattern Recognition and Computer Vision
Zhiguo Cao, Editor(s)

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