On the importance of accurate weak classifier learning for boosted weak classifiers

Gary Overett*, Lars Petersson

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Citations (Scopus)

    Abstract

    Recent work [1], has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.

    Original languageEnglish
    Title of host publication2008 IEEE Intelligent Vehicles Symposium, IV
    Pages816-821
    Number of pages6
    DOIs
    Publication statusPublished - 2008
    Event2008 IEEE Intelligent Vehicles Symposium, IV - Eindhoven, Netherlands
    Duration: 4 Jun 20086 Jun 2008

    Publication series

    NameIEEE Intelligent Vehicles Symposium, Proceedings

    Conference

    Conference2008 IEEE Intelligent Vehicles Symposium, IV
    Country/TerritoryNetherlands
    CityEindhoven
    Period4/06/086/06/08

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