Temporally Consistent Gaussian Random Field for Video Semantic Analysis
Jinhui Tang
Xian-Sheng Hua
Tao Mei
Guo-Jun Qi
Shipeng Li
Xiuqing Wu
Sci. & Technol. Univ. of China, Hefei;
This paper appears in: Image Processing, 2007. ICIP 2007. IEEE International Conference on
Publication Date: Sept. 16 2007-Oct. 19 2007
Volume: 4,
On page(s): IV - 525-IV - 528
Location: San Antonio, TX,
ISSN: 1522-4880
ISBN: 978-1-4244-1437-6
INSPEC Accession Number: 9820826
Digital Object Identifier: 10.1109/ICIP.2007.4380070
Current Version Published: 2007-11-12
Abstract
As a major family of semi-supervised learning, graph based semi-supervised learning methods have attracted lots of interests in the machine learning community as well as many application areas recently. However, for the application of video semantic annotation, these methods only consider the relations among samples in the feature space and neglect an intrinsic property of video data: the temporally adjacent video segments (e.g., shots) usually have similar semantic concept. In this paper, we adapt this temporal consistency property of video data into graph based semi-supervised learning and propose a novel method named temporally consistent Gaussian random field (TCGRF) to improve the annotation results. Experiments conducted on the TREC VID data set have demonstrated its effectiveness.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
