A New Extension of Locality Preserving Projections for Image Feature Extraction
2012 ◽
Vol 241-244
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pp. 1715-1718
Keyword(s):
This paper proposes a novel algorithm for image feature extraction, namely, the two-directional two-dimensional locality preserving projection, ((2D)2LPP), which can find an embedding from two directions that not only preserves local information and detect the intrinsic image manifold structure, but also combines the both information between rows and those between columns simultaneously. We also combine the advantages of (2D)2LPP and LDA, and propose a new framework for feature extraction as two-stage: “(2D)2LPP+LDA.” The LDA step is performed to further reduce the dimension of feature matrix in the (2D)2LPP subspace. Experimental results on ORL face databases demonstrate the effectiveness of the proposed methods.
2010 ◽
Vol 121-122
◽
pp. 391-398
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2015 ◽
Vol 32
◽
pp. 55-62
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2019 ◽
Vol 32
(10)
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pp. 6009-6024
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Keyword(s):
2003 ◽
Vol 17
(08)
◽
pp. 1325-1347
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2017 ◽
Vol 45
◽
pp. 87-94
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Keyword(s):
2012 ◽
Vol 24
(3-4)
◽
pp. 901-909
◽
2016 ◽
pp. 514-521