SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES

Falk Schmidsberger, Frieder Stolzenburg

2011

Abstract

Each object in a digital image is composed of many patches (segments) with different shapes and colors. In order to recognize an object, e.g. a table or a book, it is necessary to find out which segments are typical for which object and in which segment neighborhood they occur. If a typical segment in a characteristic neighborhood is found, this segment will be part of the object to be recognized. Typical adjacent segments for a certain object define the whole object in the image. Following this idea, we introduce a procedure that learns typical segment configurations for a given object class by training with example images of the desired object, which can be found in and downloaded from the Internet. The procedure employs methods from machine learning, namely k-means clustering and decision trees, and from computer vision, e.g. contour signatures.

References

  1. Alegre, E., Alaiz-Rodrguez, R., Barreiro, J., and Ruiz, J. (2009). Use of contour signatures and classification methods to optimize the tool life in metal machining. Estonian Journal of Engineering, 1:3-12.
  2. Bässmann, H. and Kreyss, J. (2004). Bildverarbeitung Ad Oculos. Springer, Berlin, Heidelberg, New York, 4th edition.
  3. Berry, M. J. A. and Linoff, G. (1997). Data Mining: Techniques For Marketing, Sales, and Customer Support. John Wiley & Sons Inc., New York, Chichester, Weinheim, Brisbane, Singapore, Toronto.
  4. Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media Inc., Beijing, Cambridge, Farnham, Köln, Sebastopol, Taipei, Tokyo.
  5. Han, J. and Kamber, M. (2006). Data Mining: Concepts and Techniques. Morgan Kaufman Publishers, Amsterdam, Boston, Heidelberg, London, New York, Oxford, Paris, San Diego, San Francisco, Singapore, Sydney, Tokyo, 2nd edition.
  6. Jähne, B. (2005). Digitale Bildverarbeitung. Springer, Berlin, Heidelberg, New York, 6th edition.
  7. OpenCV (2010). OpenCV (open source computer vision) library. http://opencv.willowgarage.com/wiki/.
  8. Shuang, F. (2001). Shape representation and retrieval using distance histograms. Technical report, Dept. of Computing Science, University of Alberta.
  9. SRVC (2009). Semantic robot vision challenge. http://www.semantic-robot-vision-challenge.org.
  10. Steinmüller, J. (2008). Bildanalyse. Von der Bildverarbeitung zur räumlichen Interpretation von Bildern. Springer, Berlin, Heidelberg.
Download


Paper Citation


in Harvard Style

Schmidsberger F. and Stolzenburg F. (2011). SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 670-673. DOI: 10.5220/0003188706700673


in Bibtex Style

@conference{icaart11,
author={Falk Schmidsberger and Frieder Stolzenburg},
title={SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={670-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003188706700673},
isbn={978-989-8425-40-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES
SN - 978-989-8425-40-9
AU - Schmidsberger F.
AU - Stolzenburg F.
PY - 2011
SP - 670
EP - 673
DO - 10.5220/0003188706700673