Label-specific training set construction from web resource for image annotation

  • Authors:

  • Jinhui Tang;Shuicheng Yan;Chunxia Zhao;Tat-Seng Chua;Ramesh Jain

  • Affiliations:

  • Nanjing University of Science and Technology, China;National University of Singapore, Singapore;Nanjing University of Science and Technology, China;National University of Singapore, Singapore;University of California, Irvine, United States

  • Venue:
  • Signal Processing
  • Year:
  • 2013

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Abstract

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. Locality sensitive Hashing (LSH) is applied to find the most possible region candidates of a given label efficiently. We further conduct simple human interactions to approve whether the clusters of region candidates are relevant to the given label. Here Hashing ensures the efficiency and the minimal human efforts guarantee the effectiveness of the proposed framework. Experiments conducted on a real-world dataset demonstrate that the constructed training set can result in higher accuracy for image annotation.