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2009 IEEE Conference on Computer Vision and Pattern Recognition (2009)
Miami, FL, USA
June 20, 2009 to June 25, 2009
ISBN: 978-1-4244-3992-8
pp: 2836-2843
P.V. Gehler , Dept. of Empirical Inference, Max Planck Inst. for Biol. Cybern., Tubingen, Germany
S. Nowozin , Dept. of Empirical Inference, Max Planck Inst. for Biol. Cybern., Tubingen, Germany
ABSTRACT
Most modern computer vision systems for high-level tasks, such as image classification, object recognition and segmentation, are based on learning algorithms that are able to separate discriminative information from noise. In practice, however, the typical system consists of a long pipeline of pre-processing steps, such as extraction of different kinds of features, various kinds of normalizations, feature selection, and quantization into aggregated representations such as histograms. Along this pipeline, there are many parameters to set and choices to make, and their effect on the overall system performance is a-priori unclear. In this work, we shorten the pipeline in a principled way. We move pre-processing steps into the learning system by means of kernel parameters, letting the learning algorithm decide upon suitable parameter values. Learning to optimize the pre-processing choices becomes learning the kernel parameters. We realize this paradigm by extending the recent Multiple Kernel Learning formulation from the finite case of having a fixed number of kernels which can be combined to the general infinite case where each possible parameter setting induces an associated kernel. We evaluate the new paradigm extensively on image classification and object classification tasks. We show that it is possible to learn optimal discriminative codebooks and optimal spatial pyramid schemes, consistently outperforming all previous state-of-the-art approaches.
INDEX TERMS
object classification, kernel classifiers, computer vision, learning algorithms, multiple kernel learning formulation, image classification
CITATION

P. Gehler and S. Nowozin, "Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers," 2009 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Miami, FL, USA, 2009, pp. 2836-2843.
doi:10.1109/CVPRW.2009.5206592
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