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In this paper we present a statistical learning scheme for image classification based on a mixture of old fashioned ideas and state of the art learning tools. We represent input images through large dimensional and usually sparse histograms which, depending on the task, are either color histograms or co-occurrence matrices. Support vector machines are trained on these sparse inputs directly, to solve problems like indoor/outdoor classification and cityscape retrieval from image databases. The experimental results indicate that the use of a kernel function derived from the computer vision literature leads to better recognition results than off the shelf kernels. According to our findings, it appears that image classification problems can be addressed with no need of explicit feature extraction or dimensionality reduction stages. We argue that this might be used as the starting point for developing image classification systems which can be easily tuned to a number of different tasks.