Correlated Logistic Model with Elastic Net Regularization for Multilabel Image Classification

Qiang Li, Bo Xie, Jia You, Wei Bian, Dacheng Tao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


In this paper, we present correlated logistic (CorrLog) model for multilabel image classification. CorrLog extends conventional logistic regression model into multilabel cases, via explicitly modeling the pairwise correlation between labels. In addition, we propose to learn the model parameters of CorrLog with elastic net regularization, which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification. CorrLog can be efficiently learned, though approximately, by regularized maximum pseudo likelihood estimation, and it enjoys a satisfying generalization bound that is independent of the number of labels. CorrLog performs competitively for multilabel image classification on benchmark data sets MULAN scene, MIT outdoor scene, PASCAL VOC 2007, and PASCAL VOC 2012, compared with the state-of-the-art multilabel classification algorithms.
Original languageEnglish
Article number7485813
Pages (from-to)3801-3813
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number8
Publication statusPublished - 1 Aug 2016


  • Correlated logistic model
  • elastic net
  • multilabel classification

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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