Content-Based Remote Sensing Image Retrieval Using Image Multi-feature Combination and SVM-Based Relevance Feedback

  • Lijun Zhao
  • Jiakui Tang
  • Xinju Yu
  • Yongzhi Li
  • Sujuan Mi
  • Chengwen Zhang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 124)

Abstract

In order to narrow the gap between user query concept and low-level features in content-based image retrieval, the support vector machine (SVM) based relevance feedback technique is introduced. However, remote sensing images are one kind of images with special spectral features. Relevance feedback mechanism hasn’t been widely used in content-based remote sensing image retrieval (CBRSIR). Therefore, to test the effectiveness in CBRSIR, a SVM based relevance feedback algorithm based on SVM classification theory is adopted in CBRSIR to boost remote sensing image retrieval accuracy. The experimental results show that the SVM-based relevance feedback algorithm performs well in remote sensing image retrieval and has good potential in practical applications.

Keywords

Support Vector Machine Image Retrieval Average Precision Query Image Relevance Feedback 
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 2012

Authors and Affiliations

  • Lijun Zhao
    • 1
    • 2
  • Jiakui Tang
    • 1
  • Xinju Yu
    • 1
    • 2
  • Yongzhi Li
    • 1
    • 2
  • Sujuan Mi
    • 1
    • 2
  • Chengwen Zhang
    • 1
    • 2
  1. 1.Yantai Institute of Coastal Zone ResearchChinese Academy of SciencesYantaiP.R. China
  2. 2.Graduate University of Chinese Academy of SciencesBeijingP.R. China

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