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2012 IEEE Conference on Computer Vision and Pattern Recognition (2012)
Providence, RI USA
June 16, 2012 to June 21, 2012
ISSN: 1063-6919
ISBN: 978-1-4673-1226-4
pp: 845-852
J. M. Buhmann , ETH Zurich, Zurich, Switzerland
V. Ferrari , Univ. of Edinburgh, Edinburgh, UK
A. Vezhnevets , ETH Zurich, Zurich, Switzerland
ABSTRACT
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test images instead, the method must predict a class label for every pixel. Our goal is to enable segmentation algorithms to use multiple visual cues in this weakly supervised setting, analogous to what is achieved by fully supervised methods. However, it is difficult to assess the relative usefulness of different visual cues from weakly supervised training data. We define a parametric family of structured models, were each model weights visual cues in a different way. We propose a Maximum Expected Agreement model selection principle that evaluates the quality of a model from the family without looking at superpixel labels. Searching for the best model is a hard optimization problem, which has no analytic gradient and multiple local optima. We cast it as a Bayesian optimization problem and propose an algorithm based on Gaussian processes to efficiently solve it. Our second contribution is an Extremely Randomized Hashing Forest that represents diverse superpixel features as a sparse binary vector. It enables using appearance models of visual classes that are fast at training and testing and yet accurate. Experiments on the SIFT-flow dataset show a significant improvement over previous weakly supervised methods and even over some fully supervised methods.
INDEX TERMS
optimisation, Bayes methods, Gaussian processes, image segmentation, learning (artificial intelligence), SIFT-flow dataset, weakly supervised structured output learning, semantic segmentation, multiple visual cues, maximum expected agreement model selection, hard optimization problem, Bayesian optimization problem, Gaussian process, extremely randomized hashing forest, diverse superpixel feature, sparse binary vector, appearance model, Training, Image segmentation, Visualization, Semantics, Optimization, Kernel, Measurement
CITATION

J. M. Buhmann, V. Ferrari and A. Vezhnevets, "Weakly supervised structured output learning for semantic segmentation," 2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Providence, RI USA, 2012, pp. 845-852.
doi:10.1109/CVPR.2012.6247757
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