Please use this identifier to cite or link to this item:
|Title:||Image annotation by graph-based inference with integrated multiple/single instance representations|
|Authors:||Tang, J. |
Multiple/single instance learning
|Source:||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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 7, 2018
WEB OF SCIENCETM
checked on Jan 29, 2018
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.