by C.G.M. Snoek, K.E.A. van de Sande, O. de Rooij, B. Huurnink, J.C. van Gemert, J.R.R. Uijlings, J. He, X. Li, I. Everts, V. Nedovic, M. van Liempt, R. van Balen, F. Yan, M.A. Tahir, K. Mikolajczyk, J. Kittler, M. de Rijke, J-M Geusebroek, T. Gevers, M. Worring, A.W.M. Smeulders, D.C. Koelma
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 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 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.
Reference:
C.G.M. Snoek, K.E.A. van de Sande, O. de Rooij, B. Huurnink, J.C. van Gemert, J.R.R. Uijlings, J. He, X. Li, I. Everts, V. Nedovic, M. van Liempt, R. van Balen, F. Yan, M.A. Tahir, K. Mikolajczyk, J. Kittler, M. de Rijke, J-M Geusebroek, T. Gevers, M. Worring, A.W.M. Smeulders, D.C. Koelma, "The MediaMill TRECVID 2008 Semantic Video Search Engine", In Proceedings of the 6th TRECVID Workshop, Gaithersburg, USA, 2008.
Bibtex Entry:
@INPROCEEDINGS{SnoekTRECVID08,
author = {C.G.M. Snoek and K.E.A. van de Sande 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 T. Gevers and M. Worring and A.W.M. Smeulders
and D.C. Koelma},
title = {The {MediaMill} {TRECVID} 2008 Semantic Video Search Engine},
booktitle = {Proceedings of the 6th TRECVID Workshop},
year = {2008},
address = {Gaithersburg, USA},
month = {November},
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 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 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.},
file = {mediamill-TRECVID2008-final.pdf:http\://staff.science.uva.nl/~cgmsnoek/pub/mediamill-TRECVID2008-final.pdf:PDF},
url = {http://staff.science.uva.nl/~cgmsnoek/pub/mediamill-TRECVID2008-final.pdf}
}