Active online learning for interactive segmentation using sparse Gaussian Processes
Triebel R, Stuehmer J, Souiai M, Cremers D (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Springer Verlag
Book Volume: 8753
Pages Range: 641-652
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Münster, DEU
ISBN: 9783319117515
DOI: 10.1007/978-3-319-11752-2_53
Abstract
We present an active learning framework for image segmentation with user interaction. Our system uses a sparse Gaussian Process classifier (GPC) trained on manually labeled image pixels (user scribbles) and refined in every active learning round. As a special feature, our method uses a very efficient online update rule to compute the class predictions in every round. The final segmentation of the image is computed via convex optimization. Results on a standard benchmark data set show that our algorithm is better than a recent state-of-the-art method. We also show that the queries made by the algorithm are more informative compared to randomly increasing the training data, and that our online version is much faster than the standard offline GPC inference.
Involved external institutions
Germany (DE)How to cite
APA:
Triebel, R., Stuehmer, J., Souiai, M., & Cremers, D. (2014). Active online learning for interactive segmentation using sparse Gaussian Processes. In Joachim Hornegger, Xiaoyi Jiang, Joachim Hornegger, Joachim Hornegger, Reinhard Koch (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 641-652). Münster, DEU: Springer Verlag.
MLA:
Triebel, Rudolph, et al. "Active online learning for interactive segmentation using sparse Gaussian Processes." Proceedings of the 36th German Conference on Pattern Recognition, GCPR 2014, Münster, DEU Ed. Joachim Hornegger, Xiaoyi Jiang, Joachim Hornegger, Joachim Hornegger, Reinhard Koch, Springer Verlag, 2014. 641-652.
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