Title

SOML: Sparse Online Metric Learning with Application to Image Retrieval

Publication Type

Conference Proceeding Article

Publication Date

7-2014

Abstract

Image similarity search plays a key role in many multimediaapplications, where multimedia data (such as images and videos) areusually represented in high-dimensional feature space. In thispaper, we propose a novel Sparse Online Metric Learning (SOML)scheme for learning sparse distance functions from large-scalehigh-dimensional data and explore its application to imageretrieval. In contrast to many existing distance metric learningalgorithms that are often designed for low-dimensional data, theproposed algorithms are able to learn sparse distance metrics fromhigh-dimensional data in an efficient and scalable manner. Ourexperimental results show that the proposed method achieves betteror at least comparable accuracy performance than thestate-of-the-art non-sparse distance metric learning approaches, butenjoys a significant advantage in computational efficiency andsparsity, making it more practical for real-world applications.

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

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

Additional URL

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