Multi-label active learning with chi-square statistics for image classification

Chen Ye, Jian Wu, Victor S. Sheng, Shiquan Zhao, Pengpeng Zhao, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Active learning is to select the most informative examples to request their labels. Most previous studies in active learning for multi-label classification didn't pay enough attention on label correlations. This leads to a bad performance for classification. In this paper, we proposed a chi-square statistics multi-label active learning (CSMAL) algorithm, which uses chi-square statistics to accurately evaluate correlations between labels. CSMAL considers not only positive relationships but also negative ones. It uses the average correlation between a potential label and its rest unlabeled labels as the label information for each sample-label pair. CSMAL further integrates uncertainty and label information to select example-label pairs to request labels. Our empirical results demonstrate that our proposed method CSMAL outperforms the state-of-the-art active learning methods for multi-label classification. It significantly reduces the labeling workloads and improves the performance of a classifier built.

Original languageEnglish
Title of host publicationICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages583-586
Number of pages4
ISBN (Electronic)9781450332743
DOIs
StatePublished - Jun 22 2015
Event5th ACM International Conference on Multimedia Retrieval, ICMR 2015 - Shanghai, China
Duration: Jun 23 2015Jun 26 2015

Publication series

NameICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval

Conference

Conference5th ACM International Conference on Multimedia Retrieval, ICMR 2015
CountryChina
CityShanghai
Period06/23/1506/26/15

Keywords

  • Active learning
  • Chi-square statistics
  • Label correlation
  • Multi-label image classification

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