Evidential Object Recognition Based on Information Gain Maximization

  • Thomas Reineking
  • Kerstin Schill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8764)


This paper presents an object recognition approach based on belief function inference and information gain maximization. A common problem for probabilistic object recognition models is that the parameters of the probability distributions cannot be accurately estimated using the available training data due to high dimensionality. We therefore use belief functions in order to make the reliability of the evidence provided by the training data an explicit part of the recognition model. In contrast to typical classification approaches, we consider recognition as a sequential information-gathering process where a system with dynamic beliefs actively seeks to acquire new evidence. This acquisition process is based on the principle of maximum expected information gain and enables the system to perform optimal actions for reducing uncertainty as quickly as possible. We evaluate our system on a standard object recognition dataset where we investigate the effect of the amount of training data on classification performance by comparing different methods for constructing belief functions from data.


belief functions object recognition information gain 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aregui, A., Denœux, T.: Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities. International Journal of Approximate Reasoning 49(3), 575–594 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Denœux, T.: Constructing belief functions from sample data using multinomial confidence regions. International Journal of Approximate Reasoning 42(3), 228–252 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Tech. rep., California Institute of Technology (2007)Google Scholar
  4. 4.
    Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. International Journal of Computer Vision 80(1), 3–15 (2008)CrossRefGoogle Scholar
  5. 5.
    Klir, G.J.: Uncertainty and information: foundations of generalized information theory. Wiley (2005)Google Scholar
  6. 6.
    Noë, A.: Action in Perception. MIT Press (2004)Google Scholar
  7. 7.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Reineking, T.: Particle filtering in the Dempster-Shafer theory. International Journal of Approximate Reasoning 52(8), 1124–1135 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Reineking, T.: Belief Functions: Theory and Algorithms. Ph.D. thesis, University of Bremen (February 2014)Google Scholar
  10. 10.
    Schill, K., Umkehrer, E., Beinlich, S., Krieger, G., Zetzsche, C.: Scene analysis with saccadic eye movements: Top-down and bottom-up modeling. Journal of Electronic Imaging 10(1), 152–160 (2001)CrossRefGoogle Scholar
  11. 11.
    Schill, K., Zetzsche, C., Hois, J.: A belief-based architecture for scene analysis: From sensorimotor features to knowledge and ontology. Fuzzy Sets and Systems 160(10), 1507–1516 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Smets, P.: Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning 9, 1–35 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Smets, P.: The application of the transferable belief model to diagnostic problems. International Journal of Intelligent Systems 13, 127–157 (1998)CrossRefzbMATHGoogle Scholar
  14. 14.
    Smets, P.: Decision making in the TBM: the necessity of the pignistic transformation. International Journal of Approximate Reasoning 38, 133–147 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  16. 16.
    Troffaes, M.C.: Decision making under uncertainty using imprecise probabilities. International Journal of Approximate Reasoning 45(1), 17–29 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Walley, P.: Inferences from multinomial data: learning about a bag of marbles. Journal of the Royal Statistical Society 58(1), 3–57 (1996)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks. arXiv preprint arXiv:1311.2901 (2013)Google Scholar
  19. 19.
    Zetzsche, C., Wolter, J., Schill, K.: Sensorimotor representation and knowledge-based reasoning for spatial exploration and localisation. Cognitive Processing 9, 283–297 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Reineking
    • 1
  • Kerstin Schill
    • 1
  1. 1.Cognitive NeuroinformaticsUniversity of BremenBremenGermany

Personalised recommendations