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• W306902760 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." @default.
• W306902760 created "2016-06-24" @default.
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• W306902760 date "2002-08-01" @default.
• W306902760 modified "2023-09-23" @default.
• W306902760 title "Multi-class SVMs for Image Classification using Feature Tracking" @default.
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