Local Feature Discriminant Projection

Yu, Mengyang, Shao, Ling, Zhen, Xiantong and He, Xiaofei (2016) Local Feature Discriminant Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (9). pp. 1908-1914. ISSN 0162-8828

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Official URL: http://dx.doi.org/10.1109/TPAMI.2015.2497686

Abstract

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 UIUCSports, 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.

Item Type: Article
Uncontrolled Keywords: Dimensionality reduction, Fisher vector, Image classification, Image-to-class distance, Local feature
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 13 Jan 2016 15:10
Last Modified: 09 May 2017 12:49
URI: http://nrl.northumbria.ac.uk/id/eprint/25423

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