NDLI: A Ship Recognition Method Based on Affinity Propagation

Content Provider	IEEE Xplore Digital Library
Author	Weiya Guo Xuezhi Xia
Copyright Year	2013
Description	Author affiliation: Wuhan Digital Eng. Res. Inst., Wuhan, China (Xuezhi Xia) \|\| Coll. of Inf. Technol., Harbin Eng. Univ., Harbin, China (Weiya Guo)
Abstract	Accurate target classification is the keystone of the ship targets recognition in sea battlefields. Aiming at the deficiencies of supervised and unsupervised classified methods, we present a novel scheme called semi-supervised ship target recognition based on affinity propagation (AP). In order to circumvent the problem of choosing initial points, the method introduces affinity propagation clustering to construct classification model simply and effectively. Based on the idea of semi-supervised learning, a few restrictions of labeled flows and priori manifold distribution of sampled space are abstracted. Also, manifold similarity is defined. Hence, the semi-supervised method can not only largely reduce the complexity of marking sampled flows, but also nicely improve the performance of the classified. Theoretical analysis and experimental results show that that the proposed method is robust and can get better than KNN or SVM or HDR method. With the acquirement of high recognition rate of ship targets in the sea battlefields, undoubtedly, this approach is a feasible and efficient method.
Starting Page	92
Ending Page	98
File Size	286983
Page Count	7
File Format	PDF
e-ISBN	9781479949670
DOI	10.1109/ISCC.2013.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Support vector machines Affinity propagation (AP) clustering Target recognition Semi-supervised learning Clustering algorithms Manifold similarity Feature extraction Classification algorithms Marine vehicles Ship recognition
Content Type	Text
Resource Type	Article

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