A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION

Klaus Häming, Gabriele Peters

2011

Abstract

We propose a hybrid learning system which combines two different theories of learning, namely implicit and explicit learning. They are realized by the machine learning methods of reinforcement learning and belief revision, respectively. The resulting system can be regarded as an autonomous agent which is able to learn from past experiences as well as to acquire new knowledge from its environment. We apply this agent in an object recognition task, where it learns how to recognize a 3D object despite the fact that a very similar, alternative object exists. The agent scans the viewing sphere of an object and learns how to access such a view that allows for the discrimination. We present first experiments which indicate the general applicability of the proposed hybrid learning scheme to this object recognition tasks.

References

  1. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. In 9th European Conference on Computer Vision, Graz Austria.
  2. Deinzer, F., Denzler, J., Derichs, C., and Niemann, H. (2006). Integrated viewpoint fusion and viewpoint selection for optimal object recognition. In Chanteler, M., Trucco, E., and Fisher, R., editors, British Machine Vision Conference 2006, pages 287-296, Malvern Worcs, UK. BMVA.
  3. Gordon, I. and Lowe, D. G. (2006). What and where: 3d object recognition with accurate pose. In Ponce, J., Hebert, M., Schmid, C., and Zisserman, A., editors, Toward Category-Level Object Recognition. SpringerVerlag.
  4. Häming, K. and Peters, G. (2010). An alternative approach to the revision of ordinal conditional functions in the context of multi-valued logic. In 20th International Conference on Artificial Neural Networks, Thessaloniki, Greece.
  5. Murase, H. and Nayar, S. K. (1995). Visual learning and recognition of 3-d objects from appearance. Int. J. Comput. Vision, 14(1):5-24.
  6. Peters, G. (2006). A Vision System for Interactive Object Learning. In IEEE International Conference on Computer Vision Systems (ICVS 2006), New York, USA.
  7. Schiele, B. and Crowley, J. L. (1998). Transinformation for active object recognition. In ICCV 7898: Proceedings of the Sixth International Conference on Computer Vision, page 249, Washington, DC, USA. IEEE Computer Society.
  8. Spohn, W. (2009). A survey of ranking theory. In Degrees of Belief. Springer.
  9. Sun, R., Merrill, E., and Peterson, T. (2001). From implicit skills to explicit knowledge: A bottom-up model of skill learning. In Cognitive Science, volume 25, pages 203-244.
  10. Sutton, R. S. and Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press, Cambridge.
  11. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, 1:511.
Download


Paper Citation


in Harvard Style

Häming K. and Peters G. (2011). A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8425-75-1, pages 329-332. DOI: 10.5220/0003572103290332


in Bibtex Style

@conference{icinco11,
author={Klaus Häming and Gabriele Peters},
title={A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2011},
pages={329-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003572103290332},
isbn={978-989-8425-75-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - A HYBRID LEARNING SYSTEM FOR OBJECT RECOGNITION
SN - 978-989-8425-75-1
AU - Häming K.
AU - Peters G.
PY - 2011
SP - 329
EP - 332
DO - 10.5220/0003572103290332