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Binary Codes Embedding for Fast Image Tagging with Incomplete Labels

  • Qifan Wang
  • Bin Shen
  • Shumiao Wang
  • Liang Li
  • Luo Si
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)

Abstract

Tags have been popularly utilized for better annotating, organizing and searching for desirable images. Image tagging is the problem of automatically assigning tags to images. One major challenge for image tagging is that the existing/training labels associated with image examples might be incomplete and noisy. Valuable prior work has focused on improving the accuracy of the assigned tags, but very limited work tackles the efficiency issue in image tagging, which is a critical problem in many large scale real world applications. This paper proposes a novel Binary Codes Embedding approach for Fast Image Tagging (BCE-FIT) with incomplete labels. In particular, we construct compact binary codes for both image examples and tags such that the observed tags are consistent with the constructed binary codes. We then formulate the problem of learning binary codes as a discrete optimization problem. An efficient iterative method is developed to solve the relaxation problem, followed by a novel binarization method based on orthogonal transformation to obtain the binary codes from the relaxed solution. Experimental results on two large scale datasets demonstrate that the proposed approach can achieve similar accuracy with state-of-the-art methods while using much less time, which is important for large scale applications.

Keywords

Image Tagging Binary Codes Hashing 

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Supplementary material

978-3-319-10605-2_28_MOESM1_ESM.pdf (736 kb)
Electronic Supplementary Material (PDF 737 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Qifan Wang
    • 1
  • Bin Shen
    • 1
  • Shumiao Wang
    • 1
  • Liang Li
    • 1
  • Luo Si
    • 1
  1. 1.Department of Computer SciencePurdue University West LafayetteUSA

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