Interactive boosting for image classification

Yijuan Lu, Qi Tian, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Traditional boosting method like adaboost, boosts a weak learning algorithm by updating the sample weights (the relative importance of the training samples) iteratively. In this paper, we propose to integrate feature reweighting into boosting scheme, which not only weights the samples but also weights the feature elements iteratively. To avoid overfitting problem caused by feature re-weighting on a small training data set, we also incorporate relevance feedback into boosting and propose an interactive boosting called i.Boosting. It merges adaboost, feature re-weighting and relevance feedback into one framework and exploits the favorable attributes of these methods. In this paper, i.Boosting is implemented using Adaptive Discriminant Analysis (ADA) as base classifiers. It not only enhances but also combines a set of ADA classifiers into a more powerful one. A feature re-weighting method for ADA is also proposed and integrated in i.Boosting. Extensive experiments on UCI benchmark data sets, three facial image data sets and COREL color image data sets show the superior performance of i.Boosting over AdaBoost and other state-of-the-art projection-based classifiers.

Original languageEnglish (US)
Title of host publicationMultiple Classifier Systems - 7th International Workshop, MCS 2007, Proceedings
Number of pages10
ISBN (Print)9783540724810
StatePublished - 2007
Event7th International Workshop on Multiple Classifier Systems, MCS 2007 - Prague, Czech Republic
Duration: May 23 2007May 25 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4472 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Workshop on Multiple Classifier Systems, MCS 2007
Country/TerritoryCzech Republic


  • Adaboost
  • Feature re-weighting
  • Multiple classifiers
  • Relevance feedback

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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