From Point to Set: Extend the Learning of Distance Metrics

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Authors Pengfei Zhu, Lei Zhang∗, Wangmeng Zuo, David Zhang
Journal/Conference Name 2013 International Conference on Computer Vision (ICCV 2013)
Paper Category
Paper Abstract Most of the current metric learning methods are pro-posed 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 toPSD 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 train-ing 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 multi-class 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.
Date of publication 2013
Code Programming Language MATLAB
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