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Recent research in image recognition has shown that combining multiple descriptors is a very useful way to improve classification performance. Furthermore, the use of spatial pyramids that compute descriptors at multiple spatial resolution levels generally increases the discriminative power of the descriptors. In this paper we focus on combination methods that combine multiple descriptors at multiple spatial resolution levels. A possible problem of the naive solution to create one large input vector for a machine learning classifier such as a support vector machine, is that the input vector becomes of very large dimensionality, which can increase problems of overfitting and hinder generalization performance. Therefore we propose the use of stacking support vector machines where at the first layer each support vector machine receives the input constructed by each single descriptor and is trained to compute the right …