Identifying Maps on the World Wide Web

  • Matthew Michelson
  • Aman Goel
  • Craig A. Knoblock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5266)


This paper presents an automatic approach to mining collections of maps from the Web. Our method harvests images from the Web and then classifies them as maps or non-maps by comparing them to previously classified map and non-map images using methods from Content-Based Image Retrieval (CBIR). Our approach outperforms the accuracy of the previous approach by 20% in F1-measure. Further, our method is more scalable and less costly than previous approaches that rely on more traditional machine learning techniques.


World Wide Image Retrieval Query Image Traditional Machine Learning Relevance Feedback Technique 
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 2008

Authors and Affiliations

  • Matthew Michelson
    • 1
  • Aman Goel
    • 1
  • Craig A. Knoblock
    • 1
  1. 1.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA

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