SOML: Sparse Online Metric Learning with Application to Image Retrieval

• Main Authors: GAO, Xingyu, HOI, Steven C. H., ZHANG, Yongdong, WAN, Ji, LI, Jintao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Image retrieval; person identification; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2322; https://ink.library.smu.edu.sg/context/sis_research/article/3322/viewcontent/SOLML_ImageRetrieval_2013.pdf
• Institution: Singapore Management University

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.