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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Multi-instance Methods for Partially Supervised Image Segmentation
verfasst von : Andreas Müller, Sven Behnke
Erschienen in: Partially Supervised Learning
Verlag: Springer Berlin Heidelberg
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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.