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

: Lukashevich, H.; Nowak, S.; Dunker, P.


IEEE Circuits and Systems Society; IEEE Communications Society; IEEE Signal Processing Society:
IEEE International Conference on Multimedia and Expo, ICME 2009. Proceedings. Vol.2 : New York, New York, USA, 28 June - 3 July 2009
Piscataway, NJ: IEEE, 2009
ISBN: 978-1-4244-4290-4
ISBN: 978-1-4244-4291-1
International Conference on Multimedia and Expo (ICME) <2009, New York/NY>
Fraunhofer IDMT ()
groupware; image classification; image retrieval; indexing; learning artificial intelligence; support vector machine

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.