Using n-grams Models for Visual Semantic Place Recognition

Mathieu Dubois, Emmanuelle Frenoux, Philippe Tarroux

Abstract

The aim of this paper is to present a new method for visual place recognition. Our system combines global image characterization and visual words, which allows to use efficient Bayesian filtering methods to integrate several images. More precisely, we extend the classical HMM model with techniques inspired by the field of Natural Language Processing. This paper presents our system and the Bayesian filtering algorithm. The performance of our system and the influence of the main parameters are evaluated on a standard database. The discussion highlights the interest of using such models and proposes improvements.

References

  1. Berchtold, A. (2002). High-order extensions of the double chain markov model. Technical Report 356, University of Washington.
  2. Chen, S. F. and Goodman, J. (1996). An empirical study of smoothing techniques for language modeling. In Proceedings of the 34th annual meeting on Association for Computational Linguistics.
  3. Dubois, M., Guillaume, H., Tarroux, P., and Frenoux, E. (2011). Visual place recognition using bayesian filtering with markov chains. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2011).
  4. Filliat, D. (2008). Interactive learning of visual topological navigation. In Proceedings of the 2008 IEEE International Conference on Intelligent Robots and Systems (IROS 2008).
  5. Guillaume, H., Dubois, M., Tarroux, P., and Frenoux, E. (2011). Temporal Bag-of-Words: A Generative Model for Visual Place Recognition using Temporal Integration. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP 2011).
  6. Kohonen, T. (1990). The self-organizing map. In Proceedings of the IEEE, volume 78, pages 1464-1480.
  7. Lee, L.-M. and Lee, J.-C. (2006). A study on high-order hidden markov models and applications to speech recognition. In Ali, M. and Dapoigny, R., editors, Advances in Applied Artificial Intelligence, volume 4031 of Lecture Notes in Computer Science.
  8. Manning, C. and Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
  9. Nüchter, A. and Hertzberg, J. (2008). Towards semantic maps for mobile robots. Robotics and Autonomous Systems, 56(11):915-926.
  10. Pronobis, A. and Caputo, B. (2007). Confidence-based cue integration for visual place recognition. In Proccedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.
  11. Pronobis, A., Mozos, O. M., Caputo, B., and Jenseflt, P. (2010). Multi-modal semantic place classification. The International Journal of Robotics Research, 29(2- 3):298-320.
  12. Ranganathan, A. (2010). PLISS: Detecting and labeling places using online change-point detection. In Proceedings of the 2010 Robotics: Science and Systems Conference (RSS 2010).
  13. Torralba, A., Murphy, K. P., Freeman, W. T., and Rubin., M. A. (2003). Context-based vision system for place and object recognition. In Proceedings of the Nineth IEEE International Conference on Computer Vision (ICCV 2003), volume 1, pages 273-280.
  14. Ullah, M. M., Pronobis, A., Caputo, B., Luo, J., Jensfelt, P., and Christensen, H. I. (2008). Towards robust place recognition for robot localization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2008), Pasadena, USA.
  15. Vasudevan, S., Gachter, S., Nguyen, V., and Siegwart, R. (2007). Cognitive maps for mobile robots-an object based approach. Robotics and Autonomous Systems, 55(5):359-371.
  16. Wu, J., Christensen, H., and Rehg, J. (2009). Visual place categorization: Problem, dataset, and algorithm. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009 (IROS 2009).
Download


Paper Citation


in Harvard Style

Dubois M., Frenoux E. and Tarroux P. (2013). Using n-grams Models for Visual Semantic Place Recognition . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 808-813. DOI: 10.5220/0004298708080813


in Bibtex Style

@conference{visapp13,
author={Mathieu Dubois and Emmanuelle Frenoux and Philippe Tarroux},
title={Using n-grams Models for Visual Semantic Place Recognition},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={808-813},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004298708080813},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Using n-grams Models for Visual Semantic Place Recognition
SN - 978-989-8565-47-1
AU - Dubois M.
AU - Frenoux E.
AU - Tarroux P.
PY - 2013
SP - 808
EP - 813
DO - 10.5220/0004298708080813