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| HAL : hal-00612230, version 1 |
| arXiv : 1207.3607 |
| DOI : 10.1109/IVCNZ.2009.5378367 |
| Fiche détaillée |  Récupérer au format |
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| Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th International Conference, Wellington : Nouvelle-Zélande (2009) |
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| Fusing image representations for classification using support vector machines |
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| Can Demirkesen 1, 2Hocine Cherifi 1, 3 |
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| (2009) |
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| 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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| 1 : | BIT lab (BIT Lab) |
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Université Galatasaray |
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| 2 : | Laboratoire Jean Kuntzmann (LJK) |
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CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Université Pierre-Mendès-France - Grenoble II – Institut Polytechnique de Grenoble - Grenoble Institute of Technology |
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| 3 : | Laboratoire Electronique, Informatique et Image (Le2i) |
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Université de Bourgogne – Arts et Métiers ParisTech – CNRS : UMR6306 |
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| Domaine | : | Informatique/Vision par ordinateur et reconnaissance de formes |
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| Liste des fichiers attachés à ce document : | |||||
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| hal-00612230, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00612230 | |
| oai:hal.archives-ouvertes.fr:hal-00612230 | |
| Contributeur : Hocine Cherifi | |
| Soumis le : Jeudi 28 Juillet 2011, 19:17:56 | |
| Dernière modification le : Lundi 16 Juillet 2012, 11:23:09 | |
