Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees

This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The annotation of a pixel of an unseen image is done by aggregating the annotations of its subwindows containing this pixel. The proposed method is compared to a more basic approach predicting the class of a pixel from a single window centered on that pixel and to other state-of-the-art image annotation methods. In terms of accuracy, the proposed method significantly outperforms the basic method and shows good performances with respect to the state-of-the-art, while being more generic, conceptually simpler, and of higher computational efficiency than these latter.

@InProceedings\{DMWG09,
  author       = "Dumont, Marie and Mar\'ee, Rapha{\"e}l and Wehenkel, Louis and Geurts, Pierre",
  title        = "Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees",
  booktitle    = "Proc. International Conference on Computer Vision Theory and Applications (VISAPP)",
  volume       = "2",
  pages        = "196-203",
  month        = "feb",
  year         = "2009",
  organization = "INSTICC",
  url          = "http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2009/DMWG09"
}