TRECVID 2007 High Level Feature Extraction experiments at JOANNEUM RESEARCH

This paper describes our experiments for the high level feature extraction task in TRECVid 2008. We submitted the following six runs: • A jrs1 1: Baseline, early fusion, tuned SVMs. • A jrs2 2: Early fusion, SVMs without parameter tuning. • A jrs3 3: Late fusion, tuned SVMs. • A jrs4 4: Early fusion, transductive SVM (TSVM) learning using UniverSVM with unlabeled samples on the scale of 20% of test set size. • A jrs5 5: Early fusion, transductive SVM learning using SVM TSVM with unlabeled samples on the scale of the training set size. • A jrs6 6: Early fusion, transductive SVM learning using SVM TSVM with unlabeled samples on the scale of 20% of test set size. The experiments were designed to study both the performance of various content-based features in connection with classic early and late feature fusion as well as the influence of SVM parameter tuning and learning from unlabeled test data with different implementations of transductive support vector machines (TSVMs). The results show that the time-consuming parameter tuning improves precision only marginally. Compared to standard SVMs, TSVMs did not provide overall improvement and only slight benefits for concepts with a small number of training samples.