Joint multi-label multi-instance learning for image classification
Zheng-Jun Zha
Xian-Sheng Hua
Tao Mei
Jingdong Wang
Guo-Jun Qi
Zengfu Wang
Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei;
This paper appears in: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Publication Date: 23-28 June 2008
On page(s): 1-8
Location: Anchorage, AK,
ISSN: 1063-6919
ISBN: 978-1-4244-2242-5
INSPEC Accession Number: 10139689
Digital Object Identifier: 10.1109/CVPR.2008.4587384
Current Version Published: 2008-08-05
Abstract
In real world, an image is usually associated with multiple labels which are characterized by different regions in the image. Thus image classification is naturally posed as both a multi-label learning and multi-instance learning problem. Different from existing research which has considered these two problems separately, we propose an integrated multi- label multi-instance learning (MLMIL) approach based on hidden conditional random fields (HCRFs), which simultaneously captures both the connections between semantic labels and regions, and the correlations among the labels in a single formulation. We apply this MLMIL framework to image classification and report superior performance compared to key existing approaches over the MSR Cambridge (MSRC) and Corel data sets.
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