Local Feature Discriminant Projection

Mengyang Yu, Ling Shao, Xiantong Zhen, Xiaofei He

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)
10 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1908-1914
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number9
Early online date3 Nov 2015
Publication statusPublished - 1 Sept 2016


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