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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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 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.

Item Type: Article
Uncontrolled Keywords: image classification,dimensionality reduction,local feature,image-to-class distance,fisher vector
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 28 Jan 2017 02:18
Last Modified: 22 Jul 2020 01:21
URI: https://ueaeprints.uea.ac.uk/id/eprint/62217
DOI: 10.1109/TPAMI.2015.2497686

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