Proceedings of the 8th international conference on Intelligent user interfaces
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning associative Markov networks
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
ACM SIGGRAPH 2009 papers
Reflect and correct: A misclassification prediction approach to active inference
ACM Transactions on Knowledge Discovery from Data (TKDD)
Markov Random Fields for Vision and Image Processing
Markov Random Fields for Vision and Image Processing
User-Centric Learning and Evaluation of Interactive Segmentation Systems
International Journal of Computer Vision
Transforming cluster-based segmentation for use in OpenVL by mainstream developers
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user -- a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.