Boosting kernel combination for multi-class image categorization
Alexis Lechervy, Philippe-Henri Gosselin, Fŕed́eric Precioso · 2012
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 located. We evaluate our method for image categorization by considering a state-of-the-art image database and by comparing 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.