SOML: Sparse online metric learning with application to image retrieval

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 com-putational efficiency and sparsity, making it more practical for real-world applications.