Multi-label active learning with label correlation for image classification

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

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

14 Scopus citations

Abstract

Label correlation analysis is very important for multi-label classification. And there is no study to measure the label correlation for example-label based active learning. In this paper, from a statistical point of view, we proposed a cosine similarity based multi-label active learning (CosMAL), which uses cosine similarity to accurately evaluate the correlations between all labels. It further uses the average correlation between the potential label and the other unlabeled labels as the label information for each sample-label pair. And then we select the most informativeness example-label pairs. Our empirical results demonstrate that our proposed method CosMAL outperforms the state-of-the-art active learning for multi-label classification. It significantly reduces the labeling workload and improves the performance of a classifier learned.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages3437-3441
Number of pages5
ISBN (Electronic)9781479983391
DOIs
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period09/27/1509/30/15

Keywords

  • Multi-label
  • active learning
  • example-label pair
  • image classification
  • label correlation

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