Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Fusion
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
A Bayesian sampling approach to decision fusion using hierarchicalmodels
IEEE Transactions on Signal Processing
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
Combinations of weak classifiers
IEEE Transactions on Neural Networks
VideoReach: an online video recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Contextual Video Recommendation by Multimodal Relevance and User Feedback
ACM Transactions on Information Systems (TOIS)
Efficient visual attention based framework for extracting key frames from videos
Image Communication
Shopping behavior recognition using a language modeling analogy
Pattern Recognition Letters
| Hi-index | 0.00 |
In this paper, we proposed a novel decision fusion scheme based on the psychological observations on human beings' visual and aural attention characteristics, which combines a set of decisions obtained from different data sources or features to generate better decision result. Based on studying of the “heterogeneity” and “monotonicity” properties of certain types of decision fusion issues, a set of so-called Attention Fusion Functions are devised, which are able to obtain more reasonable fusion results than typical fusion schemes. Preliminary experiment on image retrieval shows the effectiveness of the proposed fusion scheme.