Recently many research efforts have been devoted to image annotation by leveraging on the associated tags/keywords of web images as training labels. A key issue to resolve is the relatively low accuracy of the tags. In this paper, we propose a novel semi-automatic framework to construct a more accurate and effective training set from these web media resources for each label that we want to learn. Experiments conducted on a real-world dataset demonstrate that the constructed training set can result in higher accuracy for image annotation.
@article{arxiv.1107.2859,
title = {Label-Specific Training Set Construction from Web Resource for Image Annotation},
author = {Jinhui Tang and Shuicheng Yan and Tat-Seng Chua and Ramesh Jain},
journal= {arXiv preprint arXiv:1107.2859},
year = {2011}
}