Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12628
Title: From point to set : extend the learning of distance metrics
Authors: Zhu, P
Zhang, L 
Zuo, W
Zhang, D 
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the IEEE International Conference on Computer Vision, 2013, p. 2664-2671 How to cite?
Abstract: Most of the current metric learning methods are proposed for point-to-point distance (PPD) based classification. In many computer vision tasks, however, we need to measure the point-to-set distance (PSD) and even set-to-set distance (SSD) for classification. In this paper, we extend the PPD based Mahalanobis distance metric learning to PSD and SSD based ones, namely point-to-set distance metric learning (PSDML) and set-to-set distance metric learning (SSDML), and solve them under a unified optimization framework. First, we generate positive and negative sample pairs by computing the PSD and SSD between training samples. Then, we characterize each sample pair by its covariance matrix, and propose a covariance kernel based discriminative function. Finally, we tackle the PSDML and SSDML problems by using standard support vector machine solvers, making the metric learning very efficient for multiclass visual classification tasks. Experiments on gender classification, digit recognition, object categorization and face recognition show that the proposed metric learning methods can effectively enhance the performance of PSD and SSD based classification.
Description: 2013 14th IEEE International Conference on Computer Vision, ICCV 2013, Sydney, NSW, 1-8 December 2013
URI: http://hdl.handle.net/10397/12628
ISBN: 9781479928392
DOI: 10.1109/ICCV.2013.331
Appears in Collections:Conference Paper

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