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.
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Notes
- 1.
These can be either RGB pixel values or a combination of image coordinates and RGB values of the pixels. In our implementation, we use the latter, because it also provides locality information about background and foreground.
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Acknowledgment
This work was partly funded by the EU project SPENCER (ICT-2011-600877).
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Triebel, R., Stühmer, J., Souiai, M., Cremers, D. (2014). Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_53
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DOI: https://doi.org/10.1007/978-3-319-11752-2_53
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