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|Title:||Image annotation by graph-based inference with integrated multiple/single instance representations|
|Authors:||Tang, J. |
Multiple/single instance learning
|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. https://doi.org/10.1109/TMM.2009.2037373|
|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|
|Appears in Collections:||Staff Publications|
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