Semi-supervised metric learning for image classification

Jiwei Hu, Chen Sheng Sun, Kin Man Lam

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

1 Citation (Scopus)

Abstract

The k-nearest neighbor (KNN) classifier is a simple and effective method for image classification. However, its performance significantly depends on how the distance between samples is calculated. Therefore, learning an appropriate distance metric is the most important issue for the KNN-based classifiers. The distance metric can be learned from either labeled or unlabeled data. Labeled images are expensive to generate, while unlabeled images are abundant, and the label information is crucial for the performance of the learned metric. In this work, we present a semi-supervised method for learning the distance metric. We propose a semi-supervised extension to the Neighborhood Component Analysis (NCA) method, which is a supervised method especially tailored for KNN classifiers. Then, we use the learned distance metric to classify images using the KNN method. Experiment shows that our proposed method outperforms both the traditional supervised and unsupervised methods.
Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing, PCM 2010 - 11th Pacific Rim Conference on Multimedia, Proceedings
Pages728-735
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 9 Nov 2010
Event11th Pacific Rim Conference on Multimedia, PCM 2010 - Shanghai, China
Duration: 21 Sep 201024 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6298 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Pacific Rim Conference on Multimedia, PCM 2010
CountryChina
CityShanghai
Period21/09/1024/09/10

Keywords

  • distance metric
  • K-nearest neighbor
  • neighborhood component analysis
  • semi-supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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