Title :
Video annotation based on temporally consistent Gaussian random field
Author :
Tang, J. ; Hua, X.-S. ; Mei, T. ; Qi, G.-J. ; Wu, X.
Author_Institution :
Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei
Abstract :
A novel method for automatically annotating video semantics, called temporally consistent Gaussian random field (TCGRF) is proposed. Since the temporally adjacent video segments (e.g. shots) usually have a similar semantic concept, TCGRF adapts the temporal consistency property of video data into graph-based semi-supervised learning to improve the annotation results. Experiments conducted on the TRECVID data set have demonstrated its effectiveness
Keywords :
content-based retrieval; feature extraction; video signal processing; graph-based semi-supervised learning; temporally consistent Gaussian random field; video annotation; video data; video semantics;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:20073674
