The MediaMill TRECVID 2010 Semantic Video Search Engine

Publication Teaser The MediaMill TRECVID 2010 Semantic Video Search Engine
C. G. M. Snoek, K. E. A. van de Sande, O. de Rooij, B. Huurnink, E. Gavves, D. Odijk, M. de Rijke, T. Gevers, M. Worring, D. C. Koelma, A. W. M. Smeulders
In TRECVID Workshop 2010.
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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 TRECVID 2009, which uses multiple color SIFT descriptors, sparse codebooks with spatial pyramids, kernel-based machine learning, and multi-frame video processing. We improve upon this baseline system by further speeding up its execution times for both training and classification using GPU-optimized algorithms, approximated histogram intersection kernels, and several multi-frame combination methods. Being more efficient allowed us to supplement the Internet video training collection with positively labeled examples from international news broadcasts and Dutch documentary video from the TRECVID 2005-2009 benchmarks. Our experimental setup covered a huge training set of 170 thousand keyframes and a test set of 600 thousand keyframes in total. Ultimately leading to 130 robust concept detectors for video retrieval. For retrieval, a robust but limited set of concept detectors justifies the need to rely on as many auxiliary information channels as possible. For automatic known item search we therefore explore how we can learn to rank various information channels simultaneously to maximize video search results for a given topic. To further improve the video retrieval results, our interactive known item search experiments investigate how to combine metadata search and visualization into a single interface. The 2010 edition of the TRECVID benchmark has again been a fruitful participation for the MediaMill team, resulting in the top ranking for concept detection in the semantic indexing task.

Bibtex Entry
  author       = "Snoek, C. G. M. and van de Sande, K. E. A. and de Rooij, O. and Huurnink, B.
                  and Gavves, E. and Odijk, D. and de Rijke, M. and Gevers, T.
                  and Worring, M. and Koelma, D. C. and Smeulders, A. W. M.",
  title        = "The MediaMill TRECVID 2010 Semantic Video Search Engine",
  booktitle    = "TRECVID Workshop",
  year         = "2010",
  url          = "",
  pdf          = "",
  has_image    = 1
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