Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations

Authors:
Jinhui Tang;Haojie Li;Guo-Jun Qi;Tat-Seng Chua
Affiliations:
Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore;-;-;-
Venue:
IEEE Transactions on Multimedia
Year:
2010

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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.