SOML: Sparse Online Metric Learning with Application to Image Retrieval
Conference Proceeding Article
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.
Databases and Information Systems
Data Management and Analytics
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence: July 27-31, 2014, Québec City
City or Country
Menlo Park, CA
GAO, Xingyu; HOI, Steven C. H.; ZHANG, Yongdong; WAN, Ji; and LI, Jintao.
SOML: Sparse Online Metric Learning with Application to Image Retrieval. (2014). Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence: July 27-31, 2014, Québec City. 1206-1212. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2322