Active learning for semantic segmentation with expected change

A. Vezhnevets, J.M. Buhmann, V. Ferrari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We address the problem of semantic segmentation: classifying each pixel in an image according to the semantic class it belongs to (e.g. dog, road, car). Most existing methods train from fully supervised images, where each pixel is annotated by a class label. To reduce the annotation effort, recently a few weakly supervised approaches emerged. These require only image labels indicating which classes are present. Although their performance reaches a satisfactory level, there is still a substantial gap between the accuracy of fully and weakly supervised methods. We address this gap with a novel active learning method specifically suited for this setting. We model the problem as a pairwise CRF and cast active learning as finding its most informative nodes. These nodes induce the largest expected change in the overall CRF state, after revealing their true label. Our criterion is equivalent to maximizing an upper-bound on accuracy gain. Experiments on two data-sets show that our method achieves 97% percent of the accuracy of the corresponding fully supervised model, while querying less than 17% of the (super-)pixel labels.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Number of pages8
Publication statusPublished - 1 Jun 2012

Keywords / Materials (for Non-textual outputs)

  • image segmentation
  • learning (artificial intelligence)
  • active learning
  • class label
  • expected change
  • fully supervised images
  • image label
  • pairwise CRF
  • semantic segmentation
  • weakly supervised method
  • Accuracy
  • Computational modeling
  • Image segmentation
  • Labeling
  • Roads
  • Semantics
  • Training


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