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

2009

Conference Paper

ei


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.

Author(s): Gehler, PV. and Nowozin, S.
Book Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Journal: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)
Pages: 2836-2843
Year: 2009
Month: June
Day: 0
Publisher: IEEE Service Center

Department(s): Empirical Inference
Research Project(s): Machine Learning for Visual Scene Understanding
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/CVPRW.2009.5206592
Event Name: CVPR 2009
Event Place: Miami Beach, FL, USA

Address: Piscataway, NJ, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5829,
  title = {Let the Kernel Figure it Out: Principled Learning of Pre-processing for Kernel Classifiers},
  author = {Gehler, PV. and Nowozin, S.},
  journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)},
  booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  pages = {2836-2843},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = jun,
  year = {2009},
  month_numeric = {6}
}