Semantic Video Search ∗

In this paper we describe the current performance of our MediaMill system as presented in the TRECVID 2006 benchmark for video search engines. The MediaMill team participated in two tasks: concept detection and search. For concept detection we use the MediaMill Challenge as experimental platform. The MediaMill Challenge divides the generic video indexing problem into a visual-only, textualonly, early fusion, late fusion, and combined analysis experiment. We provide a baseline implementation for each experiment together with baseline results. We extract image features, on global, regional, and keypoint level, which we combine with various supervised learners. A late fusion approach of visual-only analysis methods using geometric mean was our most successful run. With this run we conquer the Challenge baseline by more than 50%. Our concept detection experiments have resulted in the best score for three concepts: i.e. desert, flag us, and charts. What is more, using LSCOM annotations, our visual-only approach generalizes well to a set of 491 concept detectors. To handle such a large thesaurus in retrieval, an engine is developed which allows users to select relevant concept detectors based on interactive browsing using advanced visualizations. Similar to previous years our best interactive search runs yield top performance, ranking 2nd and 6th overall.