  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.
