Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61912
Title: Correlated logistic model with elastic net regularization for multilabel image classification
Authors: Li, Q
Xie, B
You, J 
Bian, W
Tao, D
Keywords: Correlated logistic model
Elastic net
Multilabel classification
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2016, v. 25, no. 8, 7485813, p. 3801-3813 How to cite?
Journal: IEEE transactions on image processing 
Abstract: 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.
URI: http://hdl.handle.net/10397/61912
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2016.2577382
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