Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
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
ACM SIGGRAPH 2006 Papers
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
A comparative evaluation of interactive segmentation algorithms
Pattern Recognition
Learning an interactive segmentation system
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Cost-Sensitive Active Visual Category Learning
International Journal of Computer Vision
Toward automated evaluation of interactive segmentation
Computer Vision and Image Understanding
Markov Random Fields for Vision and Image Processing
Markov Random Fields for Vision and Image Processing
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Large-scale live active learning: Training object detectors with crawled data and crowds
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Interactive segmentation with direct connectivity priors
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
On user behaviour adaptation under interface change
Proceedings of the 19th international conference on Intelligent User Interfaces
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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. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework 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.