This article presents a method for the object classification that combines a generative template and a discriminative
classifier. The method is a variant of the support vector machine (SVM), which uses Multiple Kernel Learning (MKL). The
features are extracted from a generative template so called Active Basis template. Before using them for object
classification, we construct a visual vocabulary by clustering a set of training features according to their orientations. To
keep the spatial information, a "spatial pyramid" is used. The strength of this approach is that it combines the rich
information encoded in the generative template, the Active Basis, with the discriminative power of the SVM algorithm. We
show promising results of experiments for images from the LHI dataset.