Let the Kernel Figure it Out; Principled Learning of Pre-processing for Kernel Classifiers

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 rec...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Peter V. Gehler, Sebastian Nowozin
• Keywords: Computer Vision, CVPR 2009, Kernel Classifiers