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Efficient Approximations for Support Vector Classiers


Kienzle,  W
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Kienzle, W., & Franz, M. (2004). Efficient Approximations for Support Vector Classiers. Poster presented at 7th Tübingen Perception Conference (TWK 2004), Tübingen, Germany.

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In face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based, there are distinct advantages and drawbacks to each method: NNs are difcult to design and train but can lead to very small and efcient classiers. In comparison, SVM model selection and training is rather straightforward, and, more importantly, guaranteed to converge to a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers tend to have large representations which are inappropriate for time-critical image processing applications. In this work, we examine various existing and new methods for simplifying support vector decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical and statistical advantages of SVMs. For a given SVM solution, we compute a cascade of approximations with increasing complexities. Each classier is tuned so that the detection rate is near 100. At run-time, the rst (simplest) detector is evaluated on the whole image. Then, any subsequent classier is applied only to those positions that have been classied as positive throughout all previous stages. The false positive rate at the end equals that of the last (i.e. most complex) detector. In contrast, since many image positions are discarded by lower-complexity classiers, the average computation time per patch decreases signicantly compared to the time needed for evaluating the highest-complexity classier alone.