Modified 9DLT Matrix for Similarity Retrieval of Line-Drawing Images

  • Naveen Onkarappa
  • D. S. Guru
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

An attempt towards perception of spatial relationships existing among the generic components of line-drawing images is made for their similarity retrieval. The proposed work is based on modified 9DLT matrix representation. The conventional concept of 9DLT matrix has been tuned to accommodate multiple occurrences of identical components in images. A novel similarity measure to estimate the degree of similarity between two 9DLT matrices representing images is introduced and exploited for retrieval of line-drawing images. A database of 118 line-drawing images has been created to corroborate the effectiveness of the proposed model for similarity retrieval through an extensive experimentation.

References

  1. 1.
    Chang, C.C., Wu, T.C.: An exact match retrieval scheme based upon principal component analysis. Pattern Recognition Letters 16(5), 465–470 (1995)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Chang, C.C.: Spatial Match Retrieval of Symbolic Pictures. J. Information Science and Engineering 7 17(3), 405–422 (1991)Google Scholar
  3. 3.
    Chang, S.K., Shi, Q.Y., Yan, C.W.: Iconic Indexing by 2D Strings. IEEE Tran. on Pattern Analysis and Machine Intelligence 9(3), 413–428 (1987)CrossRefGoogle Scholar
  4. 4.
    Chang, S.K., Yan, C.W., Dimitroff, D.C., Arndt, T.: An Intelligent Image Database System. IEEE Tran. Software Engineering 14(3) (1988)Google Scholar
  5. 5.
    Duda, R.O., Hart, P.E.: Use of the Hough Transformation to Detect Lines and Curves in Pictures. Comm. ACM 15, 11–15 (1972)CrossRefGoogle Scholar
  6. 6.
    Franti, P., Mednonogov, A., Kalviainen, H.: Hough transform for rotation invariant matching of line-drawing images. In: Proceedings of the International Conf. on Pattern Recognition (ICPR), pp. 389–392 (2000)Google Scholar
  7. 7.
    Gudivada, V.N., Raghavan, V.V.: Design and Evaluation of Algorithms for Image Retrieval by Spatial Similarity. ACM Transactions on Information Systems 13(2), 115–144 (1995)CrossRefGoogle Scholar
  8. 8.
    Guru, D.S., Nagabhushan, P.: Triangular spatial relationship: a new approach for spatial knowledge representation. Pattern Recognition Letters 22(9), 999–1006 (2001)MATHCrossRefGoogle Scholar
  9. 9.
    Po-Whei, H., Chu-Hui, L.: Image Database Design Based on 9D-SPA Representation for Spatial Relations. IEEE Transactions on Knowledge and Data Engineering 16(12), 1486–1496 (2004)CrossRefGoogle Scholar
  10. 10.
    Kerbyson, D.J., Atherton, T.J.: Circle detection using Hough transform filters. In: Proceedings of Fifth International Conf. on Image Processing and its Applications, pp. 370–374 (1995)Google Scholar
  11. 11.
    Lee, S.Y., Hsu, F.J.: Spatial reasoning and similarity retrieval images using 2D-C string knowledge representation. Pattern Recognition 25(3), 305–318 (1992)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Lee, S.Y., Shan, M.K., Yang, W.P.: Similarity retrieval of iconic image database. Pattern Recognition 22(6), 675–682 (1989)CrossRefGoogle Scholar
  13. 13.
    Lee, S.Y., Hsu, F.J.: 2D C-String: A New Spatial Knowledge Representation for Image Database Systems. Pattern Recognition 23(10), 1077–1087 (1990)CrossRefGoogle Scholar
  14. 14.
    Punitha, P.: IARS: Image Archival and Retrieval Systems, Ph. D. Thesis, Dept. of Studies in Computer Science, University of Mysore, India (2006)Google Scholar
  15. 15.
    Kasturi, R., Bow, S.T., El-Masri, W., Shah, J., Gattiker, J.R., Mokate, U.B.: A System for Interpretation of Line Drawings. IEEE Tran. on Pattern Analysis and Machine Intelligence 12(10), 978–992 (1990)CrossRefGoogle Scholar
  16. 16.
    Tabbone, S., Wendling, L., Tombre, K.: Matching of graphical symbols in line-drawing images using angular signature information. International Journal on Document Analysis and Recognition 6(2), 115–125 (2003)CrossRefGoogle Scholar
  17. 17.
    Ying-Hong, W.: Image indexing and similarity retrieval based on spatial relationship model. Information Sciences 154, 39–58 (2003)CrossRefGoogle Scholar
  18. 18.
    Yu, Y., Samal, A., Seth, S.C.: A System for Recognizing a Large Class of Engineering Drawings. IEEE Tran. on Pattern Analysis and Machine Intelligence 19(8), 868–890 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Naveen Onkarappa
    • 1
  • D. S. Guru
    • 1
  1. 1.Department of Studies in Computer Science, University of Mysore, Mysore - 570 006India

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