AdaBoost on low-rank PSD matrices for metric learning

The problem of learning a proper distance or similarity metric arises in many applications such as content-based image retrieval. In this work, we propose a boosting algorithm, MetricBoost, to learn the distance metric that preserves the proximity relationships among object triplets: object i is mor...

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Bibliographic Details
Published inCVPR 2011 pp. 2617 - 2624
Main Authors Jinbo Bi, Dijia Wu, Le Lu, Meizhu Liu, Yimo Tao, Wolf, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Biomedical imaging
Computational modeling
Lungs
Matrix decomposition
Measurement
Training
Training data
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Summary:The problem of learning a proper distance or similarity metric arises in many applications such as content-based image retrieval. In this work, we propose a boosting algorithm, MetricBoost, to learn the distance metric that preserves the proximity relationships among object triplets: object i is more similar to object j than to object k. Metric-Boost constructs a positive semi-definite (PSD) matrix that parameterizes the distance metric by combining rank-one PSD matrices. Different options of weak models and combination coefficients are derived. Unlike existing proximity preserving metric learning which is generally not scalable, MetricBoost employs a bipartite strategy to dramatically reduce computation cost by decomposing proximity relationships over triplets into pair-wise constraints. Met-ricBoost outperforms the state-of-the-art on two real-world medical problems: 1. identifying and quantifying diffuse lung diseases; 2. colorectal polyp matching between different views, as well as on other benchmark datasets.
ISBN:1457703947
9781457703942
ISSN:1063-6919
DOI:10.1109/CVPR.2011.5995363