Abstract
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interac- tive search. Rather than continuing to increase the number of concept detectors available for retrieval, our TRECVID 2008 experiments focus on increasing the robustness of a small set of detectors using a bag-of-words approach. To that end, our concept detection experiments emphasize in particular the role of visual sampling, the value of color in- variant features, the influence of codebook construction, and the effectiveness of kernel-based learning parameters. For retrieval, a robust but limited set of concept detectors ne- cessitates the need to rely on as many auxiliary information channels as possible. Therefore, our automatic search ex- periments focus on predicting which information channel to trust given a certain topic, leading to a novel framework for predictive video retrieval. To improve the video retrieval re- sults further, our interactive search experiments investigate the roles of visualizing preview results for a certain browse- dimension and active learning mechanisms that learn to solve complex search topics by analysis from user brows- ing behavior. The 2008 edition of the TRECVID bench- mark has been the most successful MediaMill participation to date, resulting in the top ranking for both concept de- tection and interactive search, and a runner-up ranking for automatic retrieval. Again a lot has been learned during this year’s TRECVID campaign; we highlight the most im- portant lessons at the end of this paper.