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Hierarchical feature extraction with local neural response for image recognition
Li H.; Wei Y.; Li L.; Chen C.L.P.
2013
Source PublicationIEEE Transactions on Cybernetics
ISSN21682267
Volume43Issue:2Pages:412
AbstractIn this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model.We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms. © 2012 IEEE.
KeywordFeature extraction Hierarchical method Image recognition Local coding Neural response (NR)
DOI10.1109/TSMCB.2012.2208743
URLView the original
Language英语
The Source to ArticleScopus
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Cited Times [WOS]:29   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
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GB/T 7714
Li H.,Wei Y.,Li L.,et al. Hierarchical feature extraction with local neural response for image recognition[J]. IEEE Transactions on Cybernetics,2013,43(2):412.
APA Li H.,Wei Y.,Li L.,&Chen C.L.P..(2013).Hierarchical feature extraction with local neural response for image recognition.IEEE Transactions on Cybernetics,43(2),412.
MLA Li H.,et al."Hierarchical feature extraction with local neural response for image recognition".IEEE Transactions on Cybernetics 43.2(2013):412.
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