Advertisement

SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor

  • Raj Gupta
  • Anurag Mittal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)

Abstract

Extraction and matching of discriminative feature points in images is an important problem in computer vision with applications in image classification, object recognition, mosaicing, automatic 3D reconstruction and stereo. Features are represented and matched via descriptors that must be invariant to small errors in the localization and scale of the extracted feature point, viewpoint changes, and other kinds of changes such as illumination, image compression and blur. While currently used feature descriptors are able to deal with many of such changes, they are not invariant to a generic monotonic change in the intensities, which occurs in many cases. Furthermore, their performance degrades rapidly with many image degradations such as blur and compression where the intensity transformation is non-linear. In this paper, we present a new feature descriptor that obtains invariance to a monotonic change in the intensity of the patch by looking at orders between certain pixels in the patch. An order change between pixels indicates a difference between the patches which is penalized. Summation of such penalties over carefully chosen pixel pairs that are stable to small errors in their localization and are independent of each other leads to a robust measure of change between two features. Promising results were obtained using this approach that show significant improvement over existing methods, especially in the case of illumination change, blur and JPEG compression where the intensity of the points changes from one image to the next.

Keywords

Feature Point Local Binary Pattern Feature Descriptor Illumination Change Stability Factor 
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.
    Fabbri, R., da Fontoura Costa, L., Torelli, J.C., Bruno, O.M.: 2D Euclidean distance transform algorithms: A comparative survey. ACM Computer Survey (2008)Google Scholar
  2. 2.
    Mikolajczyk, K., Schmid, C.: An Affine Invariant Interest Point Detector. In: ECCV, pp. I-128 (2002) Google Scholar
  3. 3.
    Schaffalitzky, F., Zisserman, A.: Viewpoint Invariant Texture Matching and Wide Baseline Stereo. In: ICCV, pp. II, 636–643 (2001)Google Scholar
  4. 4.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. In: IVC, September 10, 2004, pp. 761–767 (2004)Google Scholar
  5. 5.
    Tuytelaars, T., Van Gool, L.J.: Content-Based Image Retrieval Based on Local Affinely Invariant Regions. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 493–500. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Tuytelaars, T., Van Gool, L.J.: Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions. BMVC (2000)Google Scholar
  7. 7.
    Kadir, T., Zisserman, A., Brady, M.: An Affine Invariant Salient Region Detector. In: ECCV, pp. I, 228–241 (2004)Google Scholar
  8. 8.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.J.: A Comparison of Affine Region Detectors. IJCV 1-2, 43–72 (2005)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. IJCV 2, 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Mori, G., Belongie, S., Malik, J.: Efficient Shape Matching Using Shape Contexts. PAMI 11, 1832–1837 (2005)CrossRefzbMATHGoogle Scholar
  11. 11.
    Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. PAMI 10, 1615–1630 (2005)CrossRefGoogle Scholar
  12. 12.
    Bay, H., Tuytelaars, T., Van Gool, L.J.: SURF: Speeded Up Robust Features. In: ECCV, pp. I, 404–417 (2006)Google Scholar
  13. 13.
    Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: CVPR, pp. II, 506–513 (2004)Google Scholar
  14. 14.
    Moreels, P., Perona, P.: Evaluation of Features Detectors and Descriptors Based on 3D Objects. IJCV, 263–284 (July 3, 2007)Google Scholar
  15. 15.
    Zabih, R., Woodfill, J.: Non-parametric local transforms fo computing visual correspondence. In: ECCV, pp. 151–158 (1994)Google Scholar
  16. 16.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. PAMI, 971–987 (July 7, 2002)Google Scholar
  17. 17.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. PAMI, 2037–2041 (December 12, 2006)Google Scholar
  18. 18.
    Mittal, A., Ramesh, V.: An Intensity-augmented Ordinal Measure for Visual Correspondence. In: CVPR, pp. I: 849–856 (2006)Google Scholar
  19. 19.
    Gupta, R., Mittal, A.: Illumination and Affine- Invariant Point Matching using an Ordinal Approach. In: ICCV, pp. 1–8 (2007)Google Scholar
  20. 20.
    Lepetit, V., Fua, P.: Keypoint Recognition Using Randomized Trees. PAMI, 1465–1479 (September 9, 2006)Google Scholar
  21. 21.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Raj Gupta
    • 1
  • Anurag Mittal
    • 1
  1. 1.Indian Institute of TechnologyMadrasIndia

Personalised recommendations