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

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.