Correlated Logistic Model with Elastic Net Regularization for Multilabel Image Classification

Abstract of Correlated Logistic Model for Multilabel On MAT LAB System

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.

Conclusion

Correlated Logistic Model with Elastic Net Regularization for Multilabel Image Classification,We have proposed a new MLC algorithm CorrLog and applied it to multilabel image classification. Built upon IRLs, CorrLog explicitly models the pairwise correlation between labels, and thus improves the effectiveness for MLC. Besides, by using the elastic net regularization, CorrLog is able to exploit the sparsity in both feature selection and label correlations, and thus further boost the performance of MLC.