University of Marburg at TRECVID 2008: High-Level Feature Extraction.
Markus Mühling, Ralph Ewerth, Thilo Stadelmann, Bernd Freisleben, Bing Shi · 2008
In this paper, we summarize our results for the high-level feature extraction task at TRECVID 2008. Our last year’s high-level feature extraction system was based on low-level features as well as on state-ofthe-art approaches for camera motion estimation, text detection, face detection and audio segmentation. This system served as a basis for our experiments this year and was extended in several ways. First, we paid attention to the fact that most of the concepts suffered from a small number of positive training samples while offering a huge number of negative ones. We tried to reduce this unbalance of positive and negative training samples by sub-sampling the negative instances. Furthermore, we increased the number of positive