Towards Learning Hierarchical Compositional Models in the Presence of Clutter

Author:

Mačák Jan,Drbohlav Ondřej

Publisher

Springer Berlin Heidelberg

Reference14 articles.

1. Bienenstock, E., Geman, S., Potter, D.: Compositionality, MDL priors, and object recognition. In: Mozer, M., Jordan, M.I., Petsche, T. (eds.) NIPS, pp. 838–844. MIT Press (1996)

2. Fidler, S., Boben, M., Leonardis, A.: Optimization framework for learning a hierarchical shape vocabulary for object class. In: BMVC. British Machine Vision Association (2009)

3. Fidler, S., Leonardis, A.: Towards scalable representations of object categories: Learning a hierarchy of parts. In: Proc. CVPR (2007)

4. Frey, B., Dayan, P., Hinton, G.E., Jenkin, I.M.: A simple algorithm that discovers efficient perceptual codes. In: Mechanisms, L.R.H., Mechanisms, B. (ed.) Computational and Psychophysical Mechanisms of Visual Coding, pp. 296–315. Cambridge University Press (1997)

5. Fukushima, K.: Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks 1(2), 119–130 (1988), http://www.sciencedirect.com/science/article/pii/0893608088900147

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