Affordance Mining: Forming Perception through Action

  • Liam Ellis
  • Michael Felsberg
  • Richard Bowden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6495)

Abstract

This work employs data mining algorithms to discover visual entities that are strongly associated to autonomously discovered modes of action, in an embodied agent. Mappings are learnt from these perceptual entities, onto the agents action space. In general, low dimensional action spaces are better suited to unsupervised learning than high dimensional percept spaces, allowing for structure to be discovered in the action space, and used to organise the perceptual space. Local feature configurations that are strongly associated to a particular ‘type’ of action (and not all other action types) are considered likely to be relevant in eliciting that action type. By learning mappings from these relevant features onto the action space, the system is able to respond in real time to novel visual stimuli. The proposed approach is demonstrated on an autonomous navigation task, and the system is shown to identify the relevant visual entities to the task and to generate appropriate responses.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lakoff, G., Johnson, M.: Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought. Basic Books, New York (1999)Google Scholar
  2. 2.
    Garbarini, F., Adenzato, M.: At the root of embodied cognition: Cognitive science meets neurophysiology. Brain and Cognition 56, 100–106 (2004)CrossRefGoogle Scholar
  3. 3.
    Brooks, R.A.: Intelligence without reason. In: Myopoulos, J., Reiter, R. (eds.) Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI 1991), pp. 569–595. Morgan Kaufmann publishers Inc., San Mateo (1991)Google Scholar
  4. 4.
    Gibson, J.J.: The Theory of Affordances. Lawrence Erlbaum, Mahwah (1977)Google Scholar
  5. 5.
    Efficient Mining of Frequent and Distinctive Feature Configurations. In: IEEE 11th International Conference on Computer Vision, ICCV 2007 (2007)Google Scholar
  6. 6.
    Gilbert, A., Illingworth, J., Bowden, R.: Fast realistic multi-action recognition using mined dense spatio-temporal features. In: Proc. Int. Conference Computer Vision, ICCV 2009 (2009)Google Scholar
  7. 7.
    Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: Total recall: Automatic query expansion with a generative feature model for object retrieval. In: Proceedings of the 11th International Conference on Computer Vision, Rio de Janeiro, Brazil (2007)Google Scholar
  8. 8.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  9. 9.
    Borgelt, C.: Efficient implementations of apriori and eclat (2003)Google Scholar
  10. 10.
    Granlund, G.H.: The complexity of vision. Signal Processing 74, 101–126 (1999) (invited paper)CrossRefMATHGoogle Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Sochman, J., Matas, J.: Waldboost learning for time constrained sequential detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 150–156. IEEE Computer Society, Washington (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Liam Ellis
    • 1
    • 2
  • Michael Felsberg
    • 1
  • Richard Bowden
    • 2
  1. 1.CVLLinköping UniversityLinköpingSweden
  2. 2.CVSSPUniversity of SurreyGuildfordUK

Personalised recommendations