By Topic

Automatic Video Annotation using Bayesian Inference

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Fangshi Wang ; Sch. of Comput. Inf. Technol., Beijing Jiaotong Univ. ; De Xu ; Wei Lu ; Weixin Wu

Annotating videos manually is very costly and time consuming. Human being's subjective and different understanding often lead to incomplete and inconsistent annotations and poor system performance. So it is an important topic to automatically annotate a video shot. In this paper, we propose a new approach of automatically extracting a non-fixed number of semantic concepts for a video shot. The first step is to propose a simple but efficient method to obtain the semantic candidate set (SCS) based on visual features. The second step is to select the final annotation set from the SCS by Bayesian inference. Experimental results show that our method significantly outperforms NB algorithm and KNN algorithm in automatically annotating a new video shot, and is more robust than the two algorithms

Published in:

Signal Processing, 2006 8th International Conference on  (Volume:2 )

Date of Conference:

16-20 2006