Skip to main content
SpringerLink
Log in

Combining Statistical and Symbolic Reasoning for Active Scene Categorization

  • Conference paper
Knowledge Discovery, Knowlege Engineering and Knowledge Management (IC3K 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 128))

Abstract

One of the reasons why humans are so successful at interpreting everyday situations is that they are able to combine disparate forms of knowledge. Most artificial systems, by contrast, are restricted to a single representation and hence fail to utilize the complementary nature of multiple sources of information. In this paper, we introduce an information-driven scene categorization system that integrates common sense knowledge provided by a domain ontology with a learned statistical model in order to infer a scene class from recognized objects. We show how the unspecificity of coarse logical constraints and the uncertainty of statistical relations and the object detection process can be modeled using Dempster-Shafer theory and derive the resulting belief update equations. In addition, we define an uncertainty minimization principle for adaptively selecting the most informative object detectors and present classification results for scenes from the LabelMe image database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42, 145–175 (2001)

    Article  MATH  Google Scholar 

  2. 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, 1507–1516 (2009)

    Article  MathSciNet  Google Scholar 

  3. Martínez Mozos, Ó., Triebel, R., Jensfelt, P., Rottmann, A., Burgard, W.: Supervised semantic labeling of places using information extracted from sensor data. Robotics and Autonomous Systems 55, 391–402 (2007)

    Article  Google Scholar 

  4. Kollar, T., Roy, N.: Utilizing object-object and object-scene context when planning to find things. In: International Conference on Robotics and Automation (ICRA) (2009)

    Google Scholar 

  5. Maillot, N.E., Thonnat, M.: Ontology based complex object recognition. Image and Vision Computing 26, 102–113 (2008)

    Article  Google Scholar 

  6. Russell, B., Torralba, A., Murphy, K., Freeman, W.: LabelMe: a database and web-based tool for image annotation. International Journal of Computer Vision 77, 157–173 (2008)

    Article  Google Scholar 

  7. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.: The Description Logic Handbook. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  8. Motik, B., Patel-Schneider, P.F., Grau, B.C.: OWL 2 Web Ontology Language: Direct Semantics. Technical report, W3C (2008), http://www.w3.org/TR/owl2-semantics/

  9. Horrocks, I., Kutz, O., Sattler, U.: The Even More Irresistible SROIQ. In: Knowledge Representation and Reasoning (KR). AAAI Press, Menlo Park (2006)

    Google Scholar 

  10. Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. In: Web Semantics: Science, Services and Agents on the World Wide Web, vol. 5, pp. 51–53 (2007)

    Google Scholar 

  11. Kutz, O., Lücke, D., Mossakowski, T.: Heterogeneously Structured Ontologies—Integration, Connection, and Refinement. In: Meyer, T., Orgun, M.A. (eds.) Advances in Ontologies, Proc. of the Knowledge Representation Ontology Workshop (KROW 2008), pp. 41–50. ACS (2008)

    Google Scholar 

  12. Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A.: Ontologies library. WonderWeb Deliverable D18, ISTC-CNR (2003)

    Google Scholar 

  13. Konev, B., Lutz, C., Walther, D., Wolter, F.: Formal properties of modularisation. In: Stuckenschmidt, H., Parent, C., Spaccapietra, S. (eds.) Modular Ontologies. LNCS, vol. 5445, pp. 25–66. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Vernon, D.: Cognitive vision: The case for embodied perception. Image and Vision Computing 26, 127–140 (2008)

    Article  Google Scholar 

  15. Horridge, M., Patel-Schneider, P.F.: Manchester OWL syntax for OWL 1.1. In: OWL: Experiences and Directions (OWLED 2008), DC, Gaithersberg, Maryland (2008)

    Google Scholar 

  16. Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. In: Web Semantics: Science, Services and Agents on the World Wide Web, vol. 5, pp. 51–53 (2007)

    Google Scholar 

  17. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  18. Smets, P., Kennes, R.: The transferable belief model. Artificial intelligence 66, 191–234 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  19. Smets, P.: Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning 9, 1–35 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  20. Delmotte, F., Smets, P.: Target identification based on the transferable belief model interpretation of Dempster-Shafer model. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 34, 457–471 (2004)

    Article  Google Scholar 

  21. Dubois, D., Prade, H.: On the unicity of Dempster’s rule of combination. International Journal of Intelligent Systems 1, 133–142 (1986)

    Article  MATH  Google Scholar 

  22. Smets, P.: The nature of the unnormalized beliefs encountered in the transferable belief model. In: Uncertainty in Artificial Intelligence, pp. 292–297 (1992)

    Google Scholar 

  23. Pal, N., Bezdek, J., Hemasinha, R.: Uncertainty measures for evidential reasoning II: A new measure of total uncertainty. International Journal of Approximate Reasoning 8, 1–16 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  24. Smets, P.: Decision making in the TBM: the necessity of the pignistic transformation. International Journal of Approximate Reasoning 38, 133–147 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  25. Reineking, T., Schult, N., Hois, J.: Evidential combination of ontological and statistical information for active scene classification. In: International Conference on Knowledge Engineering and Ontology Development (KEOD) (2009)

    Google Scholar 

  26. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics 28 (1998), 2000

    Google Scholar 

  27. Henderson, J., Hollingworth, A.: High-level scene perception. Annual Review of Psychology 50, 243–271 (1999)

    Article  Google Scholar 

  28. 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, 152–160 (2001)

    Article  Google Scholar 

  29. Zetzsche, C., Wolter, J., Schill, K.: Sensorimotor representation and knowledge-based reasoning for spatial exploration and localisation. Cognitive Processing 9, 283–297 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Straße 5, 28359, Bremen, Germany

    Thomas Reineking & Niclas Schult

  2. Research Center on Spatial Cognition SFB/TR 8, University of Bremen, Enrique-Schmidt-Straße 5, 28359, Bremen, Germany

    Joana Hois

Authors
  1. Thomas Reineking
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Niclas Schult
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joana Hois
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IST - Technical University of Lisbon, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Mekelweg 4, 2628, Delft, CD, The Netherlands

    Jan L. G. Dietz

  3. Informatics Research Centre, Henley Business School, University of Reading, RG6 6UD, Reading, UK

    Kecheng Liu

  4. Departament of Systems and Informatics, Polytechnic Institute of Setúbal – INSTICC, Rua do Vale de Chaves - Estefanilha, 2910-761, Setúbal, Portugal

    Joaquim Filipe

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Reineking, T., Schult, N., Hois, J. (2011). Combining Statistical and Symbolic Reasoning for Active Scene Categorization. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowlege Engineering and Knowledge Management. IC3K 2009. Communications in Computer and Information Science, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19032-2_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19032-2_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19031-5

  • Online ISBN: 978-3-642-19032-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation