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Online GRF: Semi-Automatically Labeling Objects in Video

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Abstract:

In this paper, semi-automatic methods based on Gaussian random field (GRF) for online object labeling in video were presented. With a user specified region of interest (ROI), the interested object in all of the frames can be labeled. Two methods, i.e. Updated GRF with fixed SmartLabel (UGFS) method and fixed GRF with fixed SmartLabel (FGFS) method were proposed and compared. Evaluations on object categories have indicated that the UGFS method not only improves the real time performance of object labeling in video, but also has relatively high labeling accuracy.

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Mechanical and Electronics Engineering VI View Preview

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Periodical:

Applied Mechanics and Materials (Volume 654)

Pages:

337-340

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.654.337

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Online since:

October 2014

Authors:

Qi Wang*, Lei Liu, Jian Ming Wang, Xiao Jie Duan, Xiu Yan Li

Keywords:

Gaussian Random Field, Online Labeling, Semi-Supervised Learning (SSL), SmartLabel Method

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] B. Ignas, B. Vijay and C. Roberto: IEEE Trans. Pattern Anal. Mach. Intell., Vol. 35(2013), pp.2751-2764.

Google Scholar

[2] X.J. Zhu, Z. Ghahramani and J. Lafferty, in: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, Vol. 2(2003), pp.912-919.

Google Scholar

[3] W. Wu and J. Yang, in: Proceedings of the 14th Annual ACM International Conference on Multimedia, New York, USA (2006), pp.891-900.

Google Scholar

[4] R.A. Calvo and J.M. Lee, in: the 9th Australian World Wide Web Conference, Hyatt Sanctuary Cove, Gold Coast, Australia (2003).

Google Scholar