Label-Specific Training Set Construction from Web Resource for Image   Annotation
  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. 
Jinhui Tang, Shuicheng Yan, Tat-Seng Chua and Ramesh Jain
cs.MM cs.CV