Multi-label active learning with low-rank mapping for image classification

Anqian Guo, Jian Wu, Victor S. Sheng, Pengpeng Zhao, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In multi-label image classification, each image is always associated with multiple labels and labels are usually correlated with each other. The intrinsic relation among labels can definitely contribute to classifier training. However, most previous studies on active learning for multi-label image classification purely mine label correlation based on observed label distribution. They ignore the mapping relation between examples and their labels. This mapping relation also implicates label relationship. Ignoring the mapping relation leads to an uncomprehensive label correlation estimation and results in a bad performance for classification. In this paper, we propose a novel multi-label active learning with low-rank mapping for image classification, called LMMAL, to solve this issue. More precisely, we train a low-rank mapping matrix to signify the mapping relation between the feature space and the label space of a certain multi-label dataset. Using this low-rank mapping relation, we exploit a full label correlation. Subsequently, an effective sampling strategy is designed by integrating this potential information with uncertainty to select the most informative example-label pairs. In addition, we extend LMMAL with automatic labeling (denoted as AL-LMMAL) to further reduce the annotation workload of active learning. Empirical results demonstrate the effectiveness of our approaches.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Pages259-264
Number of pages6
ISBN (Electronic)9781509060672
DOIs
StatePublished - Aug 28 2017
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: Jul 10 2017Jul 14 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2017 IEEE International Conference on Multimedia and Expo, ICME 2017
CountryHong Kong
CityHong Kong
Period07/10/1707/14/17

Keywords

  • Active learning
  • Automatic labeling
  • Label correlation
  • Multi-label image classification

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