Using one-class SVM outliers detection for verification of collaboratively tagged image training sets

Authors:
Hanna Lukashevich;Stefanie Nowak;Peter Dunker
Affiliations:
Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany;Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany;Fraunhofer Institute for Digital Media Technology, Ilmenau, Germany
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

Supervised learning requires adequately labeled training data. In this paper, we present an approach for automatic detection of outliers in image training sets using an one-class Support Vector Machine (SVM). The image sets were downloaded from photo communities solely based on their tags. We conducted four experiments to investigate if the one-class SVM can automatically differentiate between target and outlier images. As testing setup, we chose four image categories, namely Snow & Skiing, Family & Friends, Architecture & Buildings and Beach. Our experiments show that for all tests a significant tendency to remove the outliers and retain the target images is present. This offers a great possibility to gather big data sets from the web without the need for a manual review of the images.