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|a 10.1109/TPAMI.2015.2497686
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|a eng
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|a Yu, Mengyang
|e verfasserin
|4 aut
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| 245 |
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|a Local Feature Discriminant Projection
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| 264 |
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|c 2016
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|a ƒaComputermedien
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|a Date Completed 06.06.2017
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|a Date Revised 06.06.2017
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In this paper, we propose a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) for supervised dimensionality reduction of local features. LFDP is able to efficiently seek a subspace to improve the discriminability of local features for classification. We make three novel contributions. First, the proposed LFDP is a general supervised subspace learning algorithm which provides an efficient way for dimensionality reduction of large-scale local feature descriptors. Second, we introduce the Differential Scatter Discriminant Criterion (DSDC) to the subspace learning of local feature descriptors which avoids the matrix singularity problem. Third, we propose a generalized orthogonalization method to impose on projections, leading to a more compact and less redundant subspace. Extensive experimental validation on three benchmark datasets including UIUC-Sports, Scene-15 and MIT Indoor demonstrates that the proposed LFDP outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification
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|a Journal Article
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|a Shao, Ling
|e verfasserin
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| 700 |
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|a Zhen, Xiantong
|e verfasserin
|4 aut
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| 700 |
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|a He, Xiaofei
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 38(2016), 9 vom: 04. Sept., Seite 1908-14
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|x 1939-3539
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