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The MediaMill TRECVID 2008 Semantic Video Search Engine
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@MISC{Snoek_themediamill,
author = {C. G. M. Snoek and O. De Rooij and B. Huurnink and J. C. Van Gemert and J. R. R. Uijlings and J. He and X. Li and I. Everts and V. Nedovic and M. Van Liempt and R. Van Balen and F. Yan and M. A. Tahir and K. Mikolajczyk and J. Kittler and M. De Rijke and J. M. Geusebroek and Th. Gevers and M. Worring and A. W. M. Smeulders and D. C. Koelma and et al.},
title = {The MediaMill TRECVID 2008 Semantic Video Search Engine },
year = {}
}
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Abstract
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 detectors. To that end, our concept detection experiments emphasize in particular the role of sampling, the value of color invariant features, the influence of codebook construction, and the effectiveness of kernel-based learning parameters. For retrieval, a robust but limited set of concept detectors necessitates the need to rely on as many auxiliary information channels as possible. Therefore, our automatic search experiments 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 results 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 browsing behavior. The 2008 edition of the TRECVID benchmark has been the most successful MediaMill participation to date, resulting in the top ranking for both concept detection 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 important lessons at the end of this paper.
Keyphrases
semantic video search engine mediamill trecvid concept detection concept detector interactive search top ranking active learning mechanism trecvid campaign mediamill team kernel-based learning parameter many auxiliary information channel predictive video retrieval video retrieval experiment automatic search experiment certain browse-dimension video retrieval result successful mediamill participation trecvid benchmark codebook construction runner-up ranking novel framework interactive search experiment certain topic concept detection experiment preview result automatic search information channel color invariant feature limited set complex search topic important lesson automatic retrieval small set
