A Closer Look at Boosted Image Retrieval

  • Nicholas R. Howe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2728)


Margin-maximizing techniques such as boosting have been generating excitement in machine learning circles for several years now. Although these techniques offer significant improvements over previous methods on classification tasks, little research has examined the application of techniques such as boosting to the problem of retrieval from image and video databases. This paper looks at boosting for image retrieval and classification, with a comparative evaluation of several top algorithms combined in two different ways with boosting. The results show that boosting improves retrieval precision and recall (as expected), but that variations in the way boosting is applied can significantly affect the degree of improvement observed. An analysis suggests guidelines for the best way to apply boosting for retrieval with a given image representation.


Image Retrieval Image Category Image Representation Decision Boundary Color Histogram 
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 2003

Authors and Affiliations

  • Nicholas R. Howe
    • 1
  1. 1.Smith CollegeNorthampton

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