The MediaMill TRECVID 2008 Semantic Video Search Engine
Snoek, C, Sande, K, Rooij, O, Huurnink, B, Gemert, J, Uijlings, J, He, J, Li, X, Everts, I, Nedovic, V, Liempt, M, Balen, R, Yan, F, Tahir, M, Mikolajczyk, K, Kittler, J, Rijke, M, Geusebroek, J, Gevers, T, Worring, M, Smeulders, A and Koelma, D (2008) The MediaMill TRECVID 2008 Semantic Video Search Engine In: TRECVID Workshop, 2008-01-01 - ?.
Available under License : See the attached licence file.
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interac- tive 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 using a bag-of-words approach. To that end, our concept detection experiments emphasize in particular the role of visual sampling, the value of color in- variant features, the influence of codebook construction, and the effectiveness of kernel-based learning parameters. For retrieval, a robust but limited set of concept detectors ne- cessitates the need to rely on as many auxiliary information channels as possible. Therefore, our automatic search ex- periments 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 re- sults 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 brows- ing behavior. The 2008 edition of the TRECVID bench- mark has been the most successful MediaMill participation to date, resulting in the top ranking for both concept de- tection 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 im- portant lessons at the end of this paper.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Depositing User :||Symplectic Elements|
|Date Deposited :||14 Dec 2012 10:15|
|Last Modified :||09 Jun 2014 13:14|
Actions (login required)
Downloads per month over past year