Discriminative Histogram Intersection Metric Learning and Its Applications

Peng Yi Hao, Yang Xia, Xiao Xin Li, Seiichiro Kamata, Sheng Yong Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper, a novel method called discriminative histogram intersection metric learning (DHIML) is proposed for pair matching and classification. Specifically, we introduce a discrimination term for learning a metric from binary information such as same/not-same or similar/dissimilar, and then combine it with the classification error for the discrimination in classifier construction. Compared with conventional approaches, the proposed method has several advantages. 1) The histogram intersection strategy is adopted into metric learning to deal with the widely used histogram features effectively. 2) By introducing discriminative term and classification error term into metric learning, a more discriminative distance metric and a classifier can be learned together. 3) The objective function is robust to outliers and noises for both features and labels in the training. The performance of the proposed method is tested on four applications: face verification, face-track identification, face-track clustering, and image classification. Evaluations on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark, a dataset with more than 7 000 face-tracks, and Caltech-101 dataset validate the robustness and discriminability of the proposed metric learning, compared with the recent state-of-the-art approaches.

Original languageEnglish
Pages (from-to)507-519
Number of pages13
JournalJournal of Computer Science and Technology
Volume32
Issue number3
DOIs
Publication statusPublished - 2017 May 1

Keywords

  • face verification
  • image classification
  • metric learning
  • pair matching

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics

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