Advertisement

Retrieving Objects Using Local Integral Invariants

  • Alaa Halawani
  • Hashem Tamimi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)

Abstract

The use of local features in computer vision has shown to be promising. Local features have several advantages including invariance to image transformations, independence of the background, and robustness in difficult situations like partial occlusions. In this paper we suggest using local integral invariants to extract local image descriptors around interest points and use them for the retrieval task. Integral invariants capture the local structure of the neighborhood around the points where they are computed. This makes them very well suited for constructing highly-discriminative local descriptors. We study two types of kernels used for extracting the feature vectors and compare the performance of both. The dimensionality of the feature vector to be used is investigated. We also compare our results with the SIFT features. Excellent results are obtained using a dataset that contains instances of objects that are viewed in difficult situations that include clutter and occlusion.

Keywords

Feature Vector Recognition Rate Local Binary Pattern Interest Point Query Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Swain, M.J., Ballard, D.H.: Color indexing. IJCV 7, 11–32 (1991)CrossRefGoogle Scholar
  2. 2.
    Schiele, B., Crowley, J.L.: Object Recognition Using Multidimensional Receptive Field Histograms. In: ECCV., vol. 1, pp. 610–619 (1996) Google Scholar
  3. 3.
    Schmid, C., Mohr, R.: Local Grayvalue Invariants for Image Retrieval. PAMI 19, 530–535 (1997)Google Scholar
  4. 4.
    Harris, C., Stephens, M.: A Combined Corner and Edge Detector. In: Proceedings of the Fourth Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  5. 5.
    Gouet, V., Boujemaa, N.: Object-based Queries using Color Points of Interest. In: CBAIVL, Kauai, Hawaii, USA, pp. 30–36 (2001)Google Scholar
  6. 6.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Schulz-Mirbach, H.: Invariant Features for Gray Scale Images. In: 17th DAGM, Bielefeld, pp. 1–14 (1995)Google Scholar
  8. 8.
    Schael, M.: Invariant Grey Scale Features for Texture Analysis Based on Group Averaging with Relational Kernel Functions. Technical Report 1/01, University of Freiburg (2001)Google Scholar
  9. 9.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray scale and rotation invariant texture classification with local binary patterns. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 404–420. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Siggelkow, S.: Feature Historgrams for Content-Based Image Retrieval. Ph.D thesis, Albert-Ludwigs-Universität, Freiburg (2002)Google Scholar
  11. 11.
    Freidman, J.H., Bentley, J.L., Finkel, R.A.: An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematical Software 3, 209–226 (1977)CrossRefGoogle Scholar
  12. 12.
    Biber, P., Straßer, W.: Solving the Correspondence Problem by Finding Unique Features. In: 16th International Conference on Vision Interface (2003)Google Scholar
  13. 13.
    Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. In: CVPR, vol. 2, pp. 257–263 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alaa Halawani
    • 1
  • Hashem Tamimi
    • 2
  1. 1.Chair of Pattern Recognition and Image ProcessingUniversity of FreiburgFreiburgGermany
  2. 2.Computer Science Dept.University of TübingenTübingenGermany

Personalised recommendations