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Multi-class SVMs for Image Classification using Feature Tracking

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Graf,  ABA
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Wallraven,  C
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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MPIK-TR-99.pdf
(Publisher version), 176KB

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Citation

Graf, A., & Wallraven, C.(2002). Multi-class SVMs for Image Classification using Feature Tracking (99). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DF40-F
Abstract
In this paper a novel representation for image classification is proposed which exploits the temporal
information inherent in natural visual input. Image sequences are represented by a set of salient features which
are found by tracking of visual features. In the context of a multi-class classification problem this representation is
compared against a representation using only raw image data. The dataset consists of image sequences generated
from a processed version of the MPI face database. We consider two types of multi-class SVMs and benchmark
them against nearest-neighbor classifiers. By introducing a new set of SVM kernel functions we show that the
feature representation significantly outperforms the view representation.