Title

Learning robust multi-label hashing for efficient image retrieval

Publication Type

Conference Proceeding Article

Publication Date

1-2016

Abstract

Supervised hashing generally achieves superior performance over unsupervised or semi-supervised approaches by leveraging semantic labels. However, most existing supervised hashing techniques only deal with image samples with single label. Few of them properly address the practical problem concerning images with multiple labels, which is very common in real applications. In this paper, we seek to address the limitations of the existing schemes by proposing a novel approach, dubbed as Robust Multi-Label Hashing (RMLH). A label hypergraph is constructed to effectively capture high-order semantic correlations of images. And they are preserved into hashing codes with hypergraph consistency and direct label-hashing correlation. Besides, we impose a nuclear norm regularization on correlation matrix to maintain label correlations and robustly accommodate missing labels. Furthermore, an efficient algorithm based on Alternate Direction Method of Multipliers (ADMM) is developed to calculate the optimal hashing codes. Experiments demonstrate that RMLH can outperform state-of-the-art schemes and enjoy much better robustness against missing labels.

Keywords

Correlation matrix, Label hypergraph, Robust multi-label hashing

Discipline

Databases and Information Systems | Systems Architecture

Publication

Proceedings of 17th Pacific-Rim Conference on Multimedia: PCM 2016, Xi’an; China; 2016 September 15-16

First Page

285

Last Page

295

ISBN

9783319488950

Identifier

10.1007/978-3-319-48896-7_28

Publisher

Springer Verlag

City or Country

Xi’an

Additional URL

http://doi.org./10.1007/978-3-319-48896-7_28

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