A Robust Shape Retrieval Method Based on Hough-Radii

  • Xu Yang
  • Xin Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


A novel shape similarity retrieval algorithm (Hough-Radii) for 2-D objects is presented. The method uses a polar transformation of the contour points to get the shape descriptor that is invariant to translation, rotation and scaling. We take the maximum point in the generalized Hough transform (GHT) mapping array as the reference point for polar transform that is different from the traditional Centroid-Radii method where the geometric centre was taken as the origin. The effectiveness of our algorithm is illustrated in the retrieval of two databases of 99 and 216 shapes provided by Sebastian et al. The experimental results show the competitiveness of our approach to some others especially in the retrieval of partially occluded and missing images.


Query Image Shape Description Shape Match Shape Representation Contour Point 
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.


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  1. 1.
    Loncarin, S.: A survey of shape analysis techniques. Pattern Recognition 31(5), 983–1001 (1998)CrossRefGoogle Scholar
  2. 2.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)MATHCrossRefGoogle Scholar
  3. 3.
    Kim, H., Kim, J.: Region-based shape descriptor invariant to rotation, scale and translation. Signal Processing: Image Communication 16, 87–93 (2000)CrossRefGoogle Scholar
  4. 4.
    Chang, C.C., Hwang, S.M., Buehrer, D.J.: A shape recognition scheme based on relative distances of feature points from the centroid. Pattern Recognition 24, 1053–1063 (1991)CrossRefGoogle Scholar
  5. 5.
    Ozugur, T., Denizhan, Y., Panayirci, E.: Feature extraction in shape recognition using segmentation of the boundary curve. Pattern Recognition Letters 18, 1049–1056 (1997)CrossRefGoogle Scholar
  6. 6.
    Tan, K.L., Ooi, B.C., Thiang, L.F.: Indexing Shapes in Image Databases Using the Centriod-Radii Model. Data and Knowledge Engineering 32, 271–289 (2000)MATHCrossRefGoogle Scholar
  7. 7.
    Tan, K.L., Ooi, B.C., Thiang, L.F.: Retrieving similar shapes effectively and efficiently. Multimedia Tools and Applications 19(2), 111–134 (2003)CrossRefGoogle Scholar
  8. 8.
    Bernier, T., Landry, J.A.: A new method for representing and matching shapes of natural objects. Pattern Recognition 36, 1711–1723 (2003)CrossRefGoogle Scholar
  9. 9.
    Fan, S.: Shape Representation and Retrieval Using Distance Histograms. Technical Report TR 01-14, department of computer science, University of Alberta, Edmonton, Alberta, Canada (October 2001)Google Scholar
  10. 10.
    Li, D., Simske, S.: Shape Retrieval Based on Distance Ratio Distribution. Technical Report. HPL-2002-251 Google Scholar
  11. 11.
    Ballard Dana, H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 11–122 (1981)Google Scholar
  12. 12.
    Sebastian, T., Klein, P.N., Kimia, B.: Recognition of shapes by editing their shock graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 550–571 (2004)CrossRefGoogle Scholar
  13. 13.
    Milios, E., Petrakis, E.: Shape Retrieval Based on Dynamic Programming. IEEE Transactions on Image Processing 9(1), 141–146 (2000)CrossRefGoogle Scholar
  14. 14.
    Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xu Yang
    • 1
  • Xin Yang
    • 1
  1. 1.Institute of Image Processing & Pattern RecognitionShanghai Jiaotong UniversityShanghaiP.R. China

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