Semi-automatic labeling with active learning for multi-label image classification

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

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


For multi-label image classification, we use active learning to select example-label pairs to acquire labels from experts. The core of active learning is to select the most informative examples to request their labels. Most previous studies in active learning for multi-label classification have two shortcomings. One is that they didn’t pay enough attention on label correlations. The other shortcoming is that existing example-label selection methods predict all the rest labels of the selected example-label pair. This leads to a bad performance for classification when the number of the labels is large. In this paper, we propose a semi-automatic labeling multi-label active learning (SLMAL) algorithm. Firstly, SLMAL integrates uncertainty and label informativeness to select example-label pairs to request labels. Then we choose the most uncertain example-label pair and predict its partial labels using its nearest neighbor. Our empirical results demonstrate that our proposed method SLMAL 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
Pages (from-to)473-482
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2015
Event16th Pacific-Rim Conference on Multimedia, PCM 2015 - Gwangju, Korea, Republic of
Duration: Sep 16 2015Sep 18 2015


  • Active learning
  • Image classification
  • Multi-label
  • Semi-automatic labeling


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