Boosted Projection: An Ensemble of Transformation Models
Computer vision problems usually suffer from a very high dimensionality, which can make it hard to learn classifiers. A way to overcome this problem is to reduce the dimensionality of the input. This work presents a novel method for tackling this problem, referred to as Boosted Projection. It relies on the use of several projection models based on Principal Component Analysis or Partial Least Squares to build a more compact and richer data representation. We conducted experiments in two important computer vision tasks: pedestrian detection and image classification. Our experimental results demonstrate that the proposed approach outperforms many baselines and provides better results when compared to the original dimensionality reduction techniques of partial least squares.
KeywordsDimensionality reduction Machine learning Ensemble Partial Least Squares Computer vision
The authors would like to thank the Brazilian National Research Council – CNPq, the Minas Gerais Research Foundation – FAPEMIG (Grants APQ-00567-14 and PPM-00540-17) and the Coordination for the Improvement of Higher Education Personnel – CAPES (DeepEyes Project). The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce Titan X GPU used for this research.
- 5.Chollet, F.: Keras (2015). https://github.com/fchollet/keras
- 7.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
- 11.Jordão, A., Schwartz, W.R.: Oblique random forest based on partial least squares applied to pedestrian detection. In: ICIP (2016)Google Scholar
- 12.Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)Google Scholar
- 17.Schwartz, W., Kembhavi, A., Harwood, D., Davis, L.: Human detection using partial least squares analysis. In: ICCV (2009)Google Scholar
- 18.Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
- 21.Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: ICCV (2009)Google Scholar
- 22.Wold, H.: Partial least squares. In: Encyclopedia of Statistical Sciences (1985)Google Scholar