Multi-Label Dictionary Learning for Image Annotation

Xiao Yuan Jing, Fei Wu, Zhiqiang Li, Ruimin Hu, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

87 Citations (Scopus)


Image annotation has attracted a lot of research interest, and multi-label learning is an effective technique for image annotation. How to effectively exploit the underlying correlation among labels is a crucial task for multi-label learning. Most existing multi-label learning methods exploit the label correlation only in the output label space, leaving the connection between the label and the features of images untouched. Although, recently some methods attempt toward exploiting the label correlation in the input feature space by using the label information, they cannot effectively conduct the learning process in both the spaces simultaneously, and there still exists much room for improvement. In this paper, we propose a novel multi-label learning approach, named multi-label dictionary learning (MLDL) with label consistency regularization and partial-identical label embedding MLDL, which conducts MLDL and partial-identical label embedding simultaneously. In the input feature space, we incorporate the dictionary learning technique into multi-label learning and design the label consistency regularization term to learn the better representation of features. In the output label space, we design the partial-identical label embedding, in which the samples with exactly same label set can cluster together, and the samples with partial-identical label sets can collaboratively represent each other. Experimental results on the three widely used image datasets, including Corel 5K, IAPR TC12, and ESP Game, demonstrate the effectiveness of the proposed approach.
Original languageEnglish
Article number7445249
Pages (from-to)2712-2725
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number6
Publication statusPublished - 1 Jun 2016


  • Image annotation
  • input feature space
  • label consistency regularization
  • label correlation
  • multi-label dictionary learning
  • output label space
  • partial-identical label embedding

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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