@article{gao2009efficient,
abstract = {With the phenomenal growth of the online and personal video repositories, an efficient and robust example-based video search solution is required to support applications like query by clip, query by capture, and repeated clip detection. In this letter, video sequences are represented as temporal trajectories via scaling and lower dimensional representation of the video frame luminance field, and a video trajectory indexing and matching scheme is developed to support video clip search. Simulation results demonstrate that the proposed approach achieves excellent performance in both response speed and precision-recall accuracy. {\textcopyright} 2009 IEEE.},
author = {Gao, Li and Li, Zhu and Katsaggelos, Aggelos and {Li Gao} and {Zhu Li} and Katsaggelos, Aggelos},
doi = {10.1109/TCSVT.2009.2026813},
issn = {1051-8215},
journal = {IEEE transactions on circuits and systems for video technology},
keywords = {Modeling,Subspace learning,Video indexing,Video retrieval},
month = {oct},
number = {10},
pages = {1566--1570},
publisher = {IEEE},
title = {{An Efficient Video Indexing and Retrieval Algorithm Using the Luminance Field Trajectory Modeling}},
url = {http://ieeexplore.ieee.org/document/5159407/},
volume = {19},
year = {2009}
}

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