Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2014

Abstract

Image similarity search plays a key role in many multimedia applications, where multimedia data (such as images and videos) are usually represented in high-dimensional feature space. In this paper, we propose a novel Sparse Online Metric Learning (SOML) scheme for learning sparse distance functions from large-scale high-dimensional data and explore its application to image retrieval. In contrast to many existing distance metric learning algorithms that are often designed for low-dimensional data, the proposed algorithms are able to learn sparse distance metrics from high-dimensional data in an efficient and scalable manner. Our experimental results show that the proposed method achieves better or at least comparable accuracy performance than the state-of-the-art non-sparse distance metric learning approaches, but enjoys a significant advantage in computational efficiency and sparsity, making it more practical for real-world applications.

Keywords

Image retrieval, person identification

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence: July 27-31, 2014, Québec City

First Page

1206

Last Page

1212

ISBN

9781577356615

Publisher

AAAI Press

City or Country

Menlo Park, CA

Copyright Owner and License

Publisher

Additional URL

http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8369

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