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Automatic Image Semantic Annotation Based on Image-Keyword Document Model

  • Xiangdong Zhou
  • Lian Chen
  • Jianye Ye
  • Qi Zhang
  • Baile Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3568)

Abstract

This paper presents a novel method of automatic image semantic annotation. Our approach is based on the Image-Keyword Document Model (IKDM) with image features discretization. According to IKDM, the image keyword annotation is conducted using image similarity measurement based on language model from text information retrieval domain. Through the experiments on a testing set of 5000 annotated images, our approach demonstrates great improvement of annotation performance compared with the known discretization-based image annotation model such as CMRM. Our approach also performs better in annotation time compared with the continuous model such as CRM.

Keywords

Language Model Visual Feature Image Retrieval Visual Word Latent Dirichlet Allocation 
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 2005

Authors and Affiliations

  • Xiangdong Zhou
    • 1
  • Lian Chen
    • 1
  • Jianye Ye
    • 1
  • Qi Zhang
    • 2
  • Baile Shi
    • 1
  1. 1.Department of Computing and Information TechnologyFudan University ShanghaiChina
  2. 2.Department of Computer ScienceUniversity of North Carolina at Chapel Hill 

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