Abstract
In this paper, we propose a new partially supervised multi-class image segmentation algorithm. We focus on the multi-class, single-label setup, where each image is assigned one of multiple classes. We formulate the problem of image segmentation as a multi-instance task on a given set of overlapping candidate segments. Using these candidate segments, we solve the multi-instance, multi-class problem using multi-instance kernels with an SVM. This computationally advantageous approach, which requires only convex optimization, yields encouraging results on the challenging problem of partially supervised image segmentation.
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Müller, A., Behnke, S. (2012). Multi-instance Methods for Partially Supervised Image Segmentation. In: Schwenker, F., Trentin, E. (eds) Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science(), vol 7081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28258-4_12
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DOI: https://doi.org/10.1007/978-3-642-28258-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28257-7
Online ISBN: 978-3-642-28258-4
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