Top-k Multiclass SVM

Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a dire...

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Bibliographic Details
Published inarXiv.org
Main Authors Lapin, Maksim, Hein, Matthias, Schiele, Bernt
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.11.2015
Subjects
Image classification
Optimization
Upper bounds
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Summary:Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimize for top-k performance. Our generalization of the well-known multiclass SVM is based on a tight convex upper bound of the top-k error. We propose a fast optimization scheme based on an efficient projection onto the top-k simplex, which is of its own interest. Experiments on five datasets show consistent improvements in top-k accuracy compared to various baselines.
ISSN:2331-8422