Abstract
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, textual-only, 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.
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