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Object Recognition Using Hierarchical SVMs

Katarina Mele and Jasna Maver (2003) Object Recognition Using Hierarchical SVMs. In: Computer Vision Winter Workshop '03, 3-6 February 2003, Valtice, Czech Republic.

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    The paper deals with the object recognition problem. The objective is to localize and recognize the known objects in different orientations on cluttered background. As a learning tool we choose support vector machines (SVMs). To eliminate the problem caused by cluttered background we organize the image pixels in tree structures, which enable us to deal only with the object pixels. Both, one- and two-class SVMs are combined in the recognition process. One-class SVMs, used at the first stage, allow us to avoid the “nonobject” class generation as required by two-class SVM for object localization task. Two-class SVMs are applied to further resolve the recognition process when necessary. As demonstrated by experimental results the proposed method reduces the number of erroneously recognized objects.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: object recognition, SVM, one-class SVM, hierarchical SVM
    Language of Content: English
    Related URLs:
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    http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(id=3398484)Alternative location
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Divisions: Faculty of Computer and Information Science > Computer Vision Laboratory
    Item ID: 135
    Date Deposited: 16 Aug 2004
    Last Modified: 11 Dec 2013 11:33
    URI: http://eprints.fri.uni-lj.si/id/eprint/135

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