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Boosting Kernel Combination for multi-class image categorization

Abstract : In this paper, we propose a novel algorithm to design multi- class kernel functions based on an iterative combination of weak kernels in a scheme inspired from boosting framework. The method proposed in this article aims at building a new feature where the centroid for each class are optimally lo- cated. We evaluate our method for image categorization by considering a state-of-the-art image database and by compar- ing our results with reference methods. We show that on the Oxford Flower databases our approach achieves better results than previous state-of-the-art methods.
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Contributor : Philippe-Henri Gosselin <>
Submitted on : Saturday, November 17, 2012 - 6:50:32 PM
Last modification on : Monday, January 25, 2021 - 3:16:04 PM
Long-term archiving on: : Monday, February 18, 2013 - 3:42:38 AM


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  • HAL Id : hal-00753156, version 1


Alexis Lechervy, Philippe-Henri Gosselin, Frédéric Precioso. Boosting Kernel Combination for multi-class image categorization. 2012 IEEE International Conference on Image Processing (ICIP), Sep 2012, Orlando, United States. pp.4. ⟨hal-00753156⟩



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