NDLI: From Point to Set: Extend the Learning of Distance Metrics

Content Provider	IEEE Xplore Digital Library
Author	Pengfei Zhu Lei Zhang Wangmeng Zuo Zhang, D.
Copyright Year	2013
Description	Author affiliation: Hong Kong Polytech. Univ., Hong Kong, China (Pengfei Zhu; Lei Zhang; Wangmeng Zuo; Zhang, D.)
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.
Sponsorship	IEEE Comput. Soc.
Starting Page	2664
Ending Page	2671
File Size	260635
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.331
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Support vector machines Training Learning systems Covariance matrices Face recognition Visualization
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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From point to set: extend the learning of distance metrics.

Learning a mixture of sparse distance metrics for classification and dimensionality reduction

Learning hierarchical similarity metrics

Random maximum margin hashing

Enhancing Training Set for Face Detection

Classifier learning with hidden information

Deep transfer metric learning

Relative Parts: Distinctive Parts for Learning Relative Attributes

Relative attributes

From Point to Set: Extend the Learning of Distance Metrics

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From point to set: extend the learning of distance metrics.

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From Point to Set: Extend the Learning of Distance Metrics