Decision Trees and Random Subwindows for Object Recognition

In this paper, we compare five tree-based machine learning methods within a recent generic image classification framework based on random extraction and classification of subwindows. We evaluate them on three publicly available object recognition datasets (COIL-100, ETH-80, and ZuBuD). Our comparison shows that this general and conceptually simple framework yields good results when combined with ensemble of decision trees, especially when using Tree Boosting or Extra-Trees. The latter is also particularly attractive in terms of computational efficiency.

@InProceedings\{MGPW05a,
  author       = "Mar\'ee, Rapha{\"e}l and Geurts, Pierre and Piater, Justus and Wehenkel, Louis",
  title        = "Decision Trees and Random Subwindows for Object Recognition",
  booktitle    = "ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005)",
  year         = "2005",
  keywords     = "machine learning",
  url          = "http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2005/MGPW05a"
}