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  Learning an Interactive Segmentation System

Nickisch, H., Kohli, P., & Rother, C.(2009). Learning an Interactive Segmentation System. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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http://arxiv.org/abs/0912.2492 (Any fulltext)
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 Creators:
Nickisch, H1, 2, Author              
Kohli, P, Author
Rother, C, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2009-12
 Publication Status: Published in print
 Pages: 11
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 6577
 Degree: -

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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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