Local Kernel Feature Analysis (LKFA) for object recognition

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Title Local Kernel Feature Analysis (LKFA) for object recognition
Author Zhang, Baochang; Gao, Yongsheng; Zheng, Hong
Journal Name Neurocomputing
Year Published 2011
Place of publication Netherlands
Publisher Elsevier BV
Abstract This paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition. LKFA captures the nonlinear local relationship in an image via kernel functions. Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples. LKFA is designed to extract the eigenvalue features from the Hermite matrix of a local feature representation, which we have theoretically proven its robustness to noise and perturbations. Experiment results on palmprint and face recognitions demonstrated the effectiveness of the proposed LKFA that significantly improved the performance of the local feature based object recognition method.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.neucom.2010.09.008
Volume 74
Issue Number 4
Page from 575
Page to 579
ISSN 0925-2312
Date Accessioned 2011-01-17
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Artificial Intelligence and Image Processing
URI http://hdl.handle.net/10072/39788
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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