A Bayesian network-based framework for semantic image understanding
Pattern Recognition
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Automated event clustering and quality screening of consumer pictures for digital albuming
IEEE Transactions on Multimedia
Searching consumer image collections using web-based concept expansion
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploitation of time constraints for (sub-)event recognition
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
| Hi-index | 0.00 |
In this paper, we investigate event classification that is specifically developed for use in consumer family photo collections. This domain is very different from news video collections that have been the focus of research in the area of scene content classification. We determine a set of broad event classes that are relevant to personal collections. We investigate the use of a variety of high-level visual and temporal features, and determine a set of features that show good correlation with the event class. We propose a Bayesian belief network for event classification that computes the a posteriori probability of the event class given the input features. The Bayes net is trained on a large set of manually annotated consumer collections. We obtain a classification accuracy of over 70% in this challenging domain.