Abstract
In this paper, a semi-supervised multi-view teaching algorithm based on Bayesian learning is proposed for image segmentation. Beforehand, only a small amount of pixels should be classified by a teacher. The rest of the pixels are used as unlabeled examples. The algorithm uses two views and learns a separate classifier on each view. The first view contains the coordinates of the pixels and the second – the RGB values of the points in the image. Only the weaker classifier is improved by an addition of more examples to the pool of labelled examples. The performance of the algorithm for image segmentation is compared to a supervised classifier and shows very good results.
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Lazarova, G.A. (2014). Semi-supervised Image Segmentation. In: Agre, G., Hitzler, P., Krisnadhi, A.A., Kuznetsov, S.O. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2014. Lecture Notes in Computer Science(), vol 8722. Springer, Cham. https://doi.org/10.1007/978-3-319-10554-3_6
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DOI: https://doi.org/10.1007/978-3-319-10554-3_6
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