k-fold Subsampling based Sequential Backward Feature Elimination

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

    Published
    • Jeonghwan Park
    • Kang Li
    • Huiyu Zhou

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    We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy

    Documents

    DOI

    Original languageEnglish
    Title of host publicationProceedings of the 5th International Conference on Pattern Recognition Applications and Methods
    PublisherSciTePress
    Pages423-430
    Number of pages8
    ISBN (Electronic)978-989-758-173-1
    DOIs
    StatePublished - Feb 2016
    EventInternational Conference on Pattern Recognition Applications and Methods (ICPRAM) - Rome, Italy
    Duration: 24 Feb 201626 Feb 2016

    Conference

    ConferenceInternational Conference on Pattern Recognition Applications and Methods (ICPRAM)
    CountryItaly
    CityRome
    Period24/02/201626/02/2016

    ID: 22707198