A Latent Image Semantic Indexing Scheme for Image Retrieval on the Web

  • Xiaoyan Li
  • Lidan Shou
  • Gang Chen
  • Lujiang Ou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4255)


In this paper, we present a novel latent image semantic indexing scheme for efficient retrieval of WWW images. We present a hierarchical image semantic structure called HIST, which captures image semantics in an ontology tree and visual features in a set of specific semantic domains. The query algorithm works in two phases. First, the ontology is used for quickly locating the relevant semantic domains. Second, within each semantic domain, the visual features are extracted, and similarity techniques are exploited to break the “dimensionality curse”. The target images can then be efficiently retrieved with high precision. The experimental results show that HIST achieves good query performance. Therefore, our method is promising in diverse Web image retrieval.


Visual Feature Image Retrieval Query Image Relevance Feedback Salient Object 
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

  • Xiaoyan Li
    • 1
  • Lidan Shou
    • 1
  • Gang Chen
    • 1
  • Lujiang Ou
    • 1
  1. 1.Zhejiang UniversityHangzhouP.R. China

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