Abstract
The problem of semi-supervised image segmentation is frequently posed e.g. in remote sensing applications. In this setting, one aims at finding a decomposition of a given image into its constituent regions, which are typically assumed to have homogeneously distributed pixel values. In addition, it is requested that these regions can be equipped with some semantics, i.e. that they can be matched to particular land cover classes. For this purpose, class labels are provided for a small subset of the image data. The demand that the image segmentation respects those class labels implies that the segmentation algorithm should be posed as a constrained optimization problem.
We extend the Parametric Distributional Clustering (PDC) algorithm to fit into this learning framework. The resulting optimization problem is solved by constrained Deterministic Annealing. The approach is illustrated for both artificial data and real-world synthetic aperture radar (SAR) imagery.
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Hermes, L., Buhmann, J.M. (2003). Semi-supervised Image Segmentation by Parametric Distributional Clustering. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_15
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DOI: https://doi.org/10.1007/978-3-540-45063-4_15
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