Abstract
In this paper we propose an algorithm for image segmentation using graph cuts which can be used to efficiently solve labeling problems on high resolution images or image sequences. The basic idea of our method is to group large homogeneous regions to one single variable. Therefore we combine the appearance and the task specific similarity with Dempster’s theory of evidence to compute the basic belief that two pixels/groups will have the same label in the minimum energy state. Experiments on image and video segmentation show that our grouping leads to a significant speedup and memory reduction of the labeling problem. Thus large-scale labeling problems can be solved in an efficient manner with a low approximation loss.
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Scheuermann, B., Schlosser, M., Rosenhahn, B. (2013). Efficient Pixel-Grouping Based on Dempster’s Theory of Evidence for Image Segmentation. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_56
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DOI: https://doi.org/10.1007/978-3-642-37331-2_56
Publisher Name: Springer, Berlin, Heidelberg
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