Motion region-based trajectory analysis and re-ranking for video retrieval

Authors:
Bailan Feng;Juan Cao;Shouxun Lin;Yongdong Zhang;Kun Tao
Affiliations:
Laboratory of Advanced Computing Research, Institute of Computing Technology, China Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Beijing, China;Laboratory of Advanced Computing Research, Institute of Computing Technology, China Academy of Sciences, Beijing, China;Laboratory of Advanced Computing Research, Institute of Computing Technology, China Academy of Sciences, Beijing, China;Laboratory of Advanced Computing Research, Institute of Computing Technology, China Academy of Sciences, Beijing, China;Laboratory of Advanced Computing Research, Institute of Computing Technology, China Academy of Sciences and Graduate University of the Chinese Academy of Sciences, Beijing, China
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 8
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Abstract

Event-related query is playing a more and more important role in video retrieval. However, it is still a challenge to the existing video retrieval engines for lacking the effective motion analysis. In this paper, we propose a novel re-ranking scheme for video retrieval based on motion region trajectory analysis. By focusing on the changes of the primary moving regions, we construct an intuitive motion region-based trajectory descriptor (MRTD) to depict the shot activities. In the re-ranking phase, the proposed approach takes the MRTD as a motion cue and re-ranks the baseline results by motion-related query selection and MRTD-based weighting. We evaluate our method in TRECVID2007 and 2008 datasets, and observe consistent improvement over all the baselines, leading to a greatest performance gain of 42.9%, and an average gain of 17%. The experiments also show that the motion descriptor of MRTD is fruitful for a variety of features.