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Title: Image annotation by graph-based inference with integrated multiple/single instance representations
Authors: Tang, J. 
Li, H. 
Qi, G.-J.
Chua, T.-S.
Keywords: Image annotation
Multiple/single instance learning
Issue Date: 2010
Citation: Tang, J., Li, H., Qi, G.-J., Chua, T.-S. (2010). Image annotation by graph-based inference with integrated multiple/single instance representations. IEEE Transactions on Multimedia 12 (2) : 131-141. ScholarBank@NUS Repository.
Abstract: In most of the learning-based image annotation approaches, images are represented using multiple-instance (local) or single-instance (global) features. Their performances, however, are mixed as for certain concepts, the single-instance representations of images are more suitable, while for others, the multiple-instance representations are better. Thus this paper explores a unified learning framework that combines the multiple-instance and single-instance representations for image annotation. More specifically, we propose an integrated graph-based semi-supervised learning framework to utilize these two types of representations simultaneously. We further explore three strategies to convert from multiple-instance representation into a single-instance one. Experiments conducted on the COREL image dataset demonstrate the effectiveness and efficiency of the proposed integrated framework and the conversion strategies. © 2009 IEEE.
Source Title: IEEE Transactions on Multimedia
ISSN: 15209210
DOI: 10.1109/TMM.2009.2037373
Appears in Collections:Staff Publications

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