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