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Title: Conditional graphical lasso for multi-label image classification
Authors: Li, Q 
Qiao, MY
Bian, W
Tao, DC
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, US, Jun 26-Jul 1, 2016, p. 2977-2986 How to cite?
Abstract: Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correlations or lack the ability to learn image-dependent conditional label structures. In this paper, we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms.
ISBN: 978-1-4673-8851-1
ISSN: 1063-6919
DOI: 10.1109/CVPR.2016.325
Appears in Collections:Conference Paper

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