Skip to main content
SpringerLink
Log in

Combining Color and Shape Information for Appearance-Based Object Recognition Using Ultrametric Spin Glass-Markov Random Fields

  • Conference paper
  • First Online:
Book cover Pattern Recognition with Support Vector Machines (SVM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2388))

Included in the following conference series:

  • 2021 Accesses

Abstract

Shape and color information are important cues for object recognition. An ideal system should give the option to use both forms of information, as well as the option to use just one of the two. We present in this paper a kernel method that achieves this goal. It is based on results of statistical physics of disordered systems combined with Gibbs distributions via kernel functions. Experimental results on a database of 100 objects confirm the effectiveness of the proposed approach.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. D. J. Amit, “Modeling Brain Function”, Cambridge University Press, 1989.

    Google Scholar 

  2. B. Caputo, H. Niemann, “From Markov Random Fields to Associative Memories and Back: Spin Glass Markov Random Fields”, SCTV2001.

    Google Scholar 

  3. B. Caputo, J. Hornegger, D. Paulus, H. Niemann, “A Spin Glass-Markov Random Field”, Proc ICANN01 workshop on Kernel Methods, Vienna, 2001.

    Google Scholar 

  4. B. Caputo, “A new kernel method for object recognition and scene modeling: Spin Glass-Markov Random Fields”, PhD thesis, to appear.

    Google Scholar 

  5. T. Evgeniou, M. Pontil, C. Papageorgiou, T. Poggio, “Image representations for object detection using kernel classifiers” ACCV, 2000. to appear.

    Google Scholar 

  6. J. J. Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons”, Proc. Natl. Acad. Sci. USA, Vol. 81, pp. 3088–3092, 1984.

    Article  Google Scholar 

  7. C.-Y. Huang, O. I. Camps, “Object recognition using appearance-based parts and relations”, CVPR’97:877–883, 1997.

    Google Scholar 

  8. A. Jain, P. J. Flynn, editors. “Three-Dimensional Object Recognition Systems”, Amsterdam, Elsevier, 1993.

    Google Scholar 

  9. A. Leonardis, H. Bischof, “Robust recognition using eigenimages”, CVIU, 78:99–118, 2000.

    Google Scholar 

  10. J. Matas, R. Marik, J. Kittler, “On representation and matching of multi-coloured objects”, Proc ICCV95, 726–732, 1995.

    Google Scholar 

  11. B. W. Mel, “SEEMORE: combining color, shape and texture histogramming in a neurally-inspired approach to visual object recognition”, NC, 9: 777–804, 1997

    Google Scholar 

  12. M. Mezard, G. Parisi, M. Virasoro, “ Spin Glass Theory and Beyond”, World Scientific, Singapore, 1987.

    MATH  Google Scholar 

  13. J. W. Modestino, J. Zhang. “A Markov random field model-based approach to image interpretation”. PAMI, 14(6), 606–615, 1992.

    Google Scholar 

  14. H. Murase, S. K. Nayar, “Visual Learning and Recognition of 3D Objects from Appearance”, IJCV, 14(1):5–24, 1995.

    Article  Google Scholar 

  15. Nene, S. A., Nayar, S. K., Murase, H., “Columbia Object Image Library (COIL-100)”, TR CUCS-006-96, Dept. Comp. Sc., Columbia University, 1996.

    Google Scholar 

  16. E. Osuna, R. Freund, F. Girosi, “Training support vector machines: An application to face detection”, CVPR’97: 130–136, 1997.

    Google Scholar 

  17. J. Ponce, A. Zisserman, M. Hebert, “Object Representation in Computer Vision—II”, Nr. 1144 in LNCS. Springer, 1996.

    MATH  Google Scholar 

  18. Pontil, M., Verri, A. “Support Vector Machines for 3D Object Recognition”, PAMI, 20(6):637–646, 1998.

    Google Scholar 

  19. R. P. N. Rao, D. H. Ballard, “An active vision architecture based on iconic representations”, AI:461–505, 1995.

    Google Scholar 

  20. D. Roobaert, M. M. Van Hulle, “View-based 3D object recognition with support vector machines”, Proc. IEEE Workshop. on NNSP, 1999.

    Google Scholar 

  21. B. Schiele, J. L. Crowley, “Recognition without correspondence using multidimensional receptive field histograms”, IJCV, 36(1),:31–52, 2000.

    Article  Google Scholar 

  22. D. Slater, G. Healey, “Combining color and geometric information for the illumi-nantion invariant recognition of 3-D objects”, Proc ICCV95, 563–568, 1995.

    Google Scholar 

  23. M. Swain, D. Ballard, “Color indexing”, IJCV, 7(1):11–32, 1991.

    Article  Google Scholar 

  24. M. Turk, A. Pentland, “Eigenfaces for recognition”, Journal of Cognitive Neuroscience, 3(1):71–86, 1991.

    Article  Google Scholar 

  25. V. Vapnik, Statistical learning theory, J. Wiley, New York, 1998.

    MATH  Google Scholar 

  26. M. D. Wheeler, K. Ikeuchi. Sensor modeling, probabilistic hypothesis generation, and robust localization for object recognition. PAMI, 17(3):252–265, 1995.

    Google Scholar 

  27. B. Schölkopf, A. J. Smola, Learning with kernels, 2002, the MIT Press, Cambridge, MA.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Smith-Kettlewell Eye Research Institute, 2318 Fillmore Street, San Francisco, 94115, California, USA

    B. Caputo

  2. Department of Computer Science, Chair for Pattern Recognition, Erlangen-Nuremberg University, Martenstrasse 3, 91058, Erlangen, Germany

    Gy. Dorkó & H. Niemann

Authors
  1. B. Caputo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gy. Dorkó
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Niemann
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Alessandro Verri

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Caputo, B., Dorkó, G., Niemann, H. (2002). Combining Color and Shape Information for Appearance-Based Object Recognition Using Ultrametric Spin Glass-Markov Random Fields. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_8

Download citation

  • DOI: https://doi.org/10.1007/3-540-45665-1_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44016-1

  • Online ISBN: 978-3-540-45665-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation