University of Marburg at TRECVID 2010: Semantic Indexing

In this paper, we summarize our results for the semantic indexing task at TRECVID 2010. Last year, we showed that the use of object detection results as an additional input for SVM-based concept classifiers improved the overall performance. This year, we investigated whether a state-of-the-art bag-of-visual-words (BoW) approach can also be improved by adding object-based features. In this context, Multiple Kernel Learning (MKL) was applied to find the best feature weighting. The experiments revealed that the supplementation of BoW-based features with object-based features significantly improved the concept detection performance. Furthermore, we showed that a more uniform distribution of kernel weights using l2-norm MKL gained better results. Altogether, our best run achieved a mean inferred average precision of 6.96 % and we submitted the best results for the concepts “vehicle ” and “ground_vehicle”. The results of our participation in the semantic indexing task (also known as high-level feature extraction task) are presented in this section in form of the requested structured abstract. In the following sections, we describe our system for semantic indexing along with the experimental results. In Section 2, the different feature types are explained. The Multiple Kernel Learning framework is discussed in Section 3, while the experimental results are presented in Section 4. Section 5 concludes the paper. “What approach or combination of approaches did you test in each of your submitted runs?