2011/07/14 by Jinhui Tang, Tang, Jinhui, Shuicheng Yan +6
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Multimedia (cs.MM) #Text and Document Classification Technologies #cs.CV #cs.MM
paper · pdf · doi:10.48550/arxiv.1107.2859
4 pages, 5 figures
arxiv created 2011/07/14 · openalex publication_date 2011/07/14 · arxiv updated 2011/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.