Home News About Us Contact Contributors Disclaimer Privacy Policy Help FAQ

Quick Search
My eDoc
Session History
Support Wiki
Direct access to
document ID:

          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents

ID: 461762.0, MPI für biologische Kybernetik / Biologische Kybernetik
Let the Kernel Figure it Out: Principled Learning of Pre-processing for Kernel Classifiers
Authors:Gehler, P.V.; Nowozin, S.
Date of Publication (YYYY-MM-DD):2009-06
Title of Proceedings:Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)
Start Page:2836
End Page:2843
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
The scope and number of records on eDoc is subject to the collection policies defined by each institute - see "info" button in the collection browse view.