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.