Knowledge transfer in semi-automatic image interpretation

Authors:
Jun Zhou;Li Cheng;Terry Caelli;Walter F. Bischof
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;Canberra Laboratory, National ICT Australia, Canberra, ACT, Australia;Canberra Laboratory, National ICT Australia, Canberra, ACT, Australia and School of Information Science and Engineering, Australian National University, Canberra, ACT, Australia;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
Venue:
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Year:
2007

Citing 3
Cited 0

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Past, present, and future of user interface software tools

ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 1
Empirical Evaluation of User Models and User-Adapted Systems

User Modeling and User-Adapted Interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

Semi-automatic image interpretation systems utilize interactions between users and computers to adapt and update interpretation algorithms. We have studied the influence of human inputs on image interpretation by examining several knowledge transfer models. Experimental results show that the quality of the system performance depended not only on the knowledge transfer patterns but also on the user input, indicating how important it is to develop user-adapted image interpretation systems.