Integrated graph-based semi-supervised multiple/single instance learning framework for image annotation
Tang, J. Li, H. Qi, G.-J. Chua, T.-S.
Qi, G.-J.
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Abstract
Recently, many learning methods based on multiple-instance (local) or single-instance (global) representations of images have been proposed for image annotation. Their performances on image annotation, however, are mixed as for certain concepts the single-instance representations of images are more suitable, while for some other concepts the multiple-instance representations are better. Thus in this paper, we explore an 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, and explore an effective and computationally efficient strategy to convert the multiple-instance representation into a single-instance one. Experiments conducted on the Coral image dataset show the effectiveness and efficiency of the proposed integrated framework. Copyright 2008 ACM.
Keywords
Image annotation, Multiple/single instance learning
Source Title
MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops
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Date
2008
DOI
10.1145/1459359.1459446
Type
Conference Paper