The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지

Image Feature Representation Using Code Vectors for Retrieval

  • ;
  • 조혜 (조선대학교 정보통신학과) ;
  • 박종안 (조선대학교 정보통신학과) ;
  • 박승진 (전남대학교 의공학과) ;
  • 양원일 (조선대학교 전자공학과)
  • Published : 2009.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The paper presents an algorithm which uses code vectors to represent comer geometry information for searching the similar images from a database. The comers have been extracted by finding the intersections of the detected lines found using Hough transform. Taking the comer as the center coordinate, the angles of the intersecting lines are determined and are represented using code vectors. A code book has been used to code each comer geometry information and indexes to the code book are generated. For similarity measurement, the histogram of the code book indexes is used. This result in a significant small size feature matrix compared to the algorithms using color features. Experimental results show that use of code vectors is computationally efficient in similarity measurement and the comers being noise invariant produce good results in noisy environments.

Keywords

References

  1. T. Gevers and A. W. M. Smeulders, "PicToSeek: combining color and shape invariant features for image retrieval," IEEE Trans. Image Processing, vol. 9, no. 1, pp. 102-119, Jan. 2000. https://doi.org/10.1109/83.817602
  2. L. Li and W. Chen, "Corner detection and interpolation on planar curves using fuzzy reasoning," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1204-1210 Nov. 1999. https://doi.org/10.1109/34.809113
  3. S. Panchanathan and C. Huang, "Indexing and retrieval of color images using vector quantization," SPIE Proc. Applications of Digital Image Processing XXII, vol. 3808, pp. 558-568, July 1999.
  4. T. Uchiyama, M Yamaguchi, N. Ohyama, N Mukawa, and H Kaneko, "Multispectral image retrieval using vector quantization," Proc. IEEE Int. Conf. Image Processing, vol. 1, pp. 30-33, Oct. 2001.
  5. F. Idris and S. Panchanathan, "Storage and retrieval of compressed images," IEEE Consumer Electronics, vol. 41, no. 3, pp. 937-941, Jun. 1995. https://doi.org/10.1109/30.468062
  6. D. G. Sim, H. K. Kim, and R. H. Park, "Fast texture description and retrieval of DCT based compressed images," Electron. Lett., vol. 37, no. 1, pp. 18-19, Jan. 2001. https://doi.org/10.1049/el:20010035
  7. J. Z. Wang, G. Wiederhold, and O. Firschain, "Wavelet-based image indexing techniques with partial sketch retrieval capability," Proc. Forum on Research and Technology Advances in Digital Libraries, pp. 13-24, May 1997.
  8. R. Rickman and J. Stonham, "Content based image retrieval using colour tuple histograms," Proc. SPIE Storage and Retrieval for Image and Video Databases, vol. 2670, pp. 2-7, February 1996.
  9. P. V. C. Hough, "Method and means for recognising complex patterns," U. S. Patent no. 3069654, Dec. 1962.
  10. R. O. Duda and P. E. Hart, "Use of the Hough transform to detect lines and curves in pictures," Commun. ACM, vol. 15, pp. 11-15, Jan. 1972. https://doi.org/10.1145/361237.361242
  11. D. H. Ballard, "Generalizing the Hough transform to detect arbitrary shapes," Pattern Recognition, vol. 13, no. 2, pp. 111-122, Apr. 1981. https://doi.org/10.1016/0031-3203(81)90009-1
  12. E. R. Davies, "Application of the generalised Hough transform to comer detection computers and digital techniques," IEE Proc., vol. 135, no. 1, pp. 49-54, Jan. 1988. https://doi.org/10.1049/ip-d.1988.0007
  13. A. Diou, Y. Voisin, and C. Santo, "The Hough transform-a new approach," Proc. IEEE Conf. Industrial Electronics, Control, and Instrumentation, vol. 3, pp. 1612-1617, Aug. 1996.
  14. A. L. Kesidis and N. Papamarkos, "On the inverse Hough transform," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1329-1343, Dec. 1999. https://doi.org/10.1109/34.817411
  15. F. Shen and H. Wang, "Corner detection based on modified Hough transform," Pattern Recognition Letters vol. 23, no. 8, pp. 1039-1049, June 2002. https://doi.org/10.1016/S0167-8655(02)00035-1
  16. Y. H. Gu and T. Tjahjadi, "Corner based feature extraction for object retrieval," IEEE Proc. Int. Conf. Image Processing, vol. 1, pp. 119-123, Oct. 1999.
  17. A. Rattarrangsi and R. T. Chin, "Scale-based detection of corners of planar curves," IEEE Trans. Pattern Analysis and Machine Vision, vol. 14, no. 4, pp. 430-449, Apr. 1992. https://doi.org/10.1109/34.126805
  18. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabi, "Image indexing using color correlograms," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 762-768, June 1997.
  19. J. Li and J. Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, Sept. 2003. http://wang.ist.psu.edu/docs/related.shtml https://doi.org/10.1109/TPAMI.2003.1227984
  20. J. A. Park, S. J. Han, and Y. E. An, "Heuristic features for color correlogram for image retrieval," Proc. Int. Conf. Computational Sciences and Its Applications, pp. 9-13, July 2008.
  21. Y. E. An, S. B. Pan, and J. A. Park "Image retrieval based on color tone variance difference feature," Proc. Int. Conf. Machine Learning and Cybernetics, vol. 7, pp. 3777-3780, July 2008.