Path-based similarity with instance-level constraints for SemiBoost

Xiangrong Zhang; Jianshen Yu; Ting Wang; Biao Hou; L. C. Jiao

doi:10.1117/12.2031773

27 October 2013 Path-based similarity with instance-level constraints for SemiBoost

Xiangrong Zhang, Jianshen Yu, Ting Wang, Biao Hou, L. C. Jiao

Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 891911 (2013) https://doi.org/10.1117/12.2031773
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

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.

Citation Download Citation

Xiangrong Zhang, Jianshen Yu, Ting Wang, Biao Hou, and L. C. Jiao "Path-based similarity with instance-level constraints for SemiBoost", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891911 (27 October 2013); https://doi.org/10.1117/12.2031773

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