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  Multi-class SVMs for Image Classification using Feature Tracking

Graf, A., & Wallraven, C.(2002). Multi-class SVMs for Image Classification using Feature Tracking (99).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DF40-F Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DF41-D
Genre: Report

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 Creators:
Graf, ABA1, 2, Author           
Wallraven, C2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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 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.

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 Dates: 2002-08
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: Report Nr.: 99
BibTex Citekey: 1945
 Degree: -

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