Skip to main content
SpringerLink
Log in

Reducing Ambiguity in Object Recognition Using Relational Information

  • Conference paper
Book cover Computer Vision – ACCV 2010 (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6495))

Included in the following conference series:

  • 2330 Accesses

Abstract

Local feature-based object recognition methods recognize learned objects by unordered local feature matching followed by verification. However, the matching between unordered feature sets might be ambiguous as the number of objects increases, because multiple similar features can be observed in different objects. In this context, we present a new method for textured object recognition based on relational information between local features. To efficiently reduce ambiguity, we represent objects using the Attributed Relational Graph. Robust object recognition is achieved by the inexact graph matching. Here, we propose a new method for building graphs and define robust attributes for nodes and edges of the graph, which are the most important factors in the graph-based object representation, and also propose a cost function for graph matching. Dependent on the proposed attributes, the proposed framework can be applied to both single-image-based and stereo-image-based object recognition.

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. Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention. International Journal of Computer Vision 11, 283–318 (1993)

    Article  Google Scholar 

  2. Shokoufandeh, A., Marsic, I., Dickinson, S.: View-based object matching. In: IEEE International Conference on Computer Vision, pp. 588–595 (1998)

    Google Scholar 

  3. Schmid, C., Bauckhage, R.M.: Evaluation of interest point detectors. International Journal of Computer Vision 37, 151–172 (2000)

    Article  MATH  Google Scholar 

  4. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)

    Article  Google Scholar 

  5. Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 506–513 (2004)

    Google Scholar 

  6. Kim, S., Yoon, K.J., Kweon, I.S.: Object recognition using a generalized robust invariant feature and gestalt’s law of proximity and similarity. Pattern Recognition 41, 726–741 (2008)

    Article  MATH  Google Scholar 

  7. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  8. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)

    Article  Google Scholar 

  9. Tico, M., Kuosmanen, P.: Fingerpoint matching using an orientation-based minutia descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1009–1014 (2003)

    Article  Google Scholar 

  10. Schmid, C.: A structured probabilistic model for recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 485–490 (1999)

    Google Scholar 

  11. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271 (2003)

    Google Scholar 

  12. Moreels, P., Maire, M., Perona, P.: Recognition by probabilistic hypothesis construction. In: Eurographics Symposium on Rendering, pp. 55–68 (2004)

    Google Scholar 

  13. Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13, 111–122 (1981)

    Article  MATH  Google Scholar 

  14. Lamdan, Y., Wolfson, H.J.: Geometric hashing: A general and efficient model-based recognition scheme. In: International Conference on Computer Vision, pp. 238–249 (1988)

    Google Scholar 

  15. Dorkó, G., Schmid, C.: Selection of scale-invariant parts for object class recognition. In: International Conference on Computer Vision, pp. 634–639 (2003)

    Google Scholar 

  16. Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering object categories in image collections. In: International Conference on Computer Vision (2005)

    Google Scholar 

  17. Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structure for object recognition. International Journal of Computer Vision 61, 55–79 (2005)

    Article  Google Scholar 

  18. Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 10–17 (2005)

    Google Scholar 

  19. Gold, S., Rangarajan, A.: A graduated assignment algorithm for graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 377–388 (1996)

    Article  Google Scholar 

  20. Caetano, T.S., Cheng, L., Le, Q.V., Smola, A.J.: Learning graph matching. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  21. Torresani, L., Kolmogorov, V., Rother, C.: Feature correspondence via graph matching: Models and global optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Graciano, A., Cesar Jr., R., Bloch, I.: Graph-based object tracking using structural pattern recognition. In: Proc. of SIBGRAPI, pp. 179–186 (2007)

    Google Scholar 

  23. Carneiro, G., Jepson, A.D.: Object recognition using flexible groups of local features. Technical Report CSRG-481 (2004)

    Google Scholar 

  24. Conte, D., Foggia, P., Sansone, C., Vento, M.: How and why pattern recognition and computer vision applications use graph. Springer, New York (2007)

    Book  Google Scholar 

  25. Guerra Filho, G.: Disambiguating the recognition of 3d objects. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2278–2285 (2009)

    Google Scholar 

  26. Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43, 29–44 (2001)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Vision Laboratory, School of Information and Communication, GIST, Republic of Korea

    Kuk-Jin Yoon & Min-Gil Shin

Authors
  1. Kuk-Jin Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min-Gil Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, Technion – Israel Institute of Technology, 32000, Haifa, Israel

    Ron Kimmel

  2. The University of Auckland, 37 Kohimarama Road, 1071, Mission Bay, Auckland, New Zealand

    Reinhard Klette

  3. National Institute of Informatics, 1018430, Chiyoda, Tokyo, Japan

    Akihiro Sugimoto

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yoon, KJ., Shin, MG. (2011). Reducing Ambiguity in Object Recognition Using Relational Information. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19282-1_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19281-4

  • Online ISBN: 978-3-642-19282-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation