Shrinkage expansion adaptive metric learning

Qilong Wang, Wangmeng Zuo, Lei Zhang, Peihua Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

15 Citations (Scopus)


Conventional pairwise constrained metric learning methods usually restrict the distance between samples of a similar pair to be lower than a fixed upper bound, and the distance between samples of a dissimilar pair higher than a fixed lower bound. Such fixed bound based constraints, however, may not work well when the intra- and inter-class variations are complex. In this paper, we propose a shrinkage expansion adaptive metric learning (SEAML) method by defining a novel shrinkage-expansion rule for adaptive pairwise constraints. SEAML is very effective in learning metrics from data with complex distributions. Meanwhile, it also suggests a new rule to assess the similarity between a pair of samples based on whether their distance is shrunk or expanded after metric learning. Our extensive experimental results demonstrated that SEAML achieves better performance than state-of-the-art metric learning methods. In addition, the proposed shrinkage-expansion adaptive pairwise constraints can be readily applied to many other pairwise constrained metric learning algorithms, and boost significantly their performance in applications such as face verification on LFW and PubFig databases.
Original languageEnglish
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer Verlag
Number of pages16
EditionPART 7
ISBN (Print)9783319105833
Publication statusPublished - 1 Jan 2014
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: 6 Sep 201412 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 7
Volume8695 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th European Conference on Computer Vision, ECCV 2014


  • adaptive bound constraints
  • face verification
  • pairwise constrained metric learning
  • Shrinkage-expansion rule

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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