Automatic Image Annotation by Mining the Web

  • Zhiguo Gong
  • Qian Liu
  • Jingbai Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4081)


Automatic image annotation has been becoming an attractive research subject. Most current image annotation methods are based on training techniques. The major weaknesses of such solutions include limited annotation vocabulary and labor-intensive involvement. However, Web images possess a lot of texts, and rich annotation of samples is provided. Therefore, this report provides a novel image annotation method by mining the Web that term-image correlation is obtained from the Web not by learning. Without question, there are many noises in that relation, and some cleaning works are necessary. In the system, entropy weighting and image clustering technique are employed. Our experiment results show that our solution can achieve a satisfactory performance.


Visual Feature Image Annotation Latent Dirichlet Allocation Model Entropy Weighting Mean Reciprocal Rank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhiguo Gong
    • 1
  • Qian Liu
    • 1
  • Jingbai Zhang
    • 1
  1. 1.Faculty of Science and TechnologyUniversity of MacauMacaoPRC

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