Paper Info
Title
Automatic Video Annotation With Adaptive Number Of Key Words
Abstract
Retrieving videos using key words requires obtaining the semantic features of the videos. Most work reported in the literature focuses on annotating a video shot with a fixed number of key words, no matter how much information is contained in the video shot. In this paper, we propose a new approach to automatically annotate a video shot with an adaptive number of annotation key words according to the richness of the video content. A Semantic Candidate Set (SCS) with fixed size is discovered using visual features. Then the final annotation set, which has an unfixed number of key words, is obtained from the SCS by using Bayesian Inference, which combines static and dynamic inference to remove the irrelevant candidate key words. We have applied our approach to video retrieval. The experiments demonstrate that video retrieval using our annotation approach outperforms retrieval using a fixed number of annotation words.
Year
DOI
Venue
2008
10.1109/ICPR.2008.4761418
19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6
Keywords
Field
DocType
bayesian network,image classification,learning artificial intelligence,bayesian inference,statistical analysis
Bayesian inference,Computer science,Video annotation,Image retrieval,Artificial intelligence,Contextual image classification,Computer vision,Annotation,Video retrieval,Information retrieval,Pattern recognition,Inference,Candidate key
Conference
ISSN
Citations 
PageRank 
1051-4651
6
0.45
References 
Authors
6
5
Name
Order
Citations
PageRank
Fangshi Wang1214.74
Wei Lu26617.41
Jingen Liu380734.41
Mubarak Shah416522943.74
De Xu515813.08