Multi-instance methods for partially supervised image segmentation

Authors:
Andreas Müller;Sven Behnke
Affiliations:
Autonomous Intelligent Systems Department of Computer Science, University of Bonn, Bonn, Germany;Autonomous Intelligent Systems Department of Computer Science, University of Bonn, Bonn, Germany
Venue:
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Year:
2011

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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.