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In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interactive 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(More)
In this paper we describe our TRECVID 2010 video retrieval experiments. The MediaMill team participated in three tasks: semantic indexing, known-item search, and instance search. The starting point for the MediaMill concept detection approach is our top-performing bag-of-words system of last year, which uses multiple color SIFT descrip-tors, sparse(More)
In this technical demonstration we showcase the MediaMill system. A search engine that facilitates access to news video archives at a semantic level. The core of the system is an unprecedented lexicon of 100 automatically detected semantic concepts. Based on this lexicon we demonstrate how users can obtain highly relevant retrieval results using(More)
In this paper we describe our TRECVID 2007 experiments. The MediaMill team participated in two tasks: concept detection and search. For concept detection we extract region-based image features, on grid, keypoint, and segmentation level, which we combine with various supervised learners. In addition, we explore the utility of temporal image features. A late(More)
I nteractive prototypes are often the best way to convince an audience of a new multime-dia technology's possible impact. Because of its dynamic audiovisual nature, a multimedia application demonstration communicates applied science more effectively than a static description in a journal publication would. Ideally, a multimedia demonstrator grasps the(More)
Various query methods for video search exist. Because of the semantic gap each method has its limitations. We argue that for effective retrieval query methods need to be combined at retrieval time. However, switching query methods often involves a change in query and browsing interface, which puts a heavy burden on the user. In this paper, we propose a(More)
In this paper we present the methods underlying the Medi-aMill semantic video search engine. The basis for the engine is a semantic indexing process which is currently based on a lexicon of 491 concept detectors. To support the user in navigating the collection, the system defines a visual similarity space, a semantic similarity space, a semantic thread(More)