2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
Jiwen Lu , Advanced Digital Sciences Center, Singapore
Gang Wang , Advanced Digital Sciences Center, Singapore
Weihong Deng , School of ICE, Beijing University of Posts and Telecommunications, China
Pierre Moulin , Advanced Digital Sciences Center, Singapore
Jie Zhou , Department of Automation, Tsinghua University, Beijing, China
In this paper, we propose a multi-manifold deep metric learning (MMDML) method for image set classification, which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations. Motivated by the fact that manifold can be effectively used to model the nonlinearity of samples in each image set and deep learning has demonstrated superb capability to model the nonlinearity of samples, we propose a MMDML method to learn multiple sets of nonlinear transformations, one set for each object class, to nonlinearly map multiple sets of image instances into a shared feature subspace, under which the manifold margin of different class is maximized, so that both discriminative and class-specific information can be exploited, simultaneously. Our method achieves the state-of-the-art performance on five widely used datasets.
J. Lu, G. Wang, W. Deng, P. Moulin and J. Zhou, "Multi-manifold deep metric learning for image set classification," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 1137-1145.