Fusing image representations for classification using support vector machines

Abstract : In order to improve classification accuracy different image representations are usually combined. This can be done by using two different fusing schemes. In feature level fusion schemes, image representations are combined before the classification process. In classifier fusion, the decisions taken separately based on individual representations are fused to make a decision. In this paper the main methods derived for both strategies are evaluated. Our experimental results show that classifier fusion performs better. Specifically Bayes belief integration is the best performing strategy for image classification task.
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Communication dans un congrès
Donald Bailey. IVCNZ '09 - 24th International Conference Image and Vision Computing New Zealand, Nov 2009, Wellington, New Zealand. IEEE, pp.437-441, 2009, <10.1109/IVCNZ.2009.5378367>
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Contributeur : Hocine Cherifi <>
Soumis le : jeudi 28 juillet 2011 - 19:17:56
Dernière modification le : lundi 29 février 2016 - 16:26:52
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Can Demirkesen, Hocine Cherifi. Fusing image representations for classification using support vector machines. Donald Bailey. IVCNZ '09 - 24th International Conference Image and Vision Computing New Zealand, Nov 2009, Wellington, New Zealand. IEEE, pp.437-441, 2009, <10.1109/IVCNZ.2009.5378367>. <hal-00612230>

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