Untitled Document
Untitled Document
User Name
You are from : ( )  
Untitled Document
Untitled Document

International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

This paper provides an image shape representation technique known as Adaptive Kernel Density Feature Points Estimator (AKDFPE). In this method, the density of feature points within defined rings (bandwidth) around the centroid of the image is obtained in the form of a vector. The AKDFPE is then applied to the vector of the image. AKDFPE is invariant to translation, scale and rotation. This method of image representation shows improved retrieval rate when compared to Kernel Density Feature Points Estimator (KDFPE) method. Analytic analysis is done to justify our method, which was compared with the KDFPE to prove its robustness.

Keywords :

: Kernel Density Function, Similarity, Image Representation, Segmentation, Density Histogram

References :

  1. M. X. Ribeiro, et al., "Statistical Association Rules and Relevance Feedback: Power Allies to Improve the Retrieval of Medical Images," Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems, 2006.
  2. D. Zhang and G. Lu, "Review of shape representation and description techniques," Pattern Recognition Society, vol. 37, pp. 1-19, 2004.
  3. Y. Li and L. Guan, "An effective shape descriptor for the retrieval of natural image collections," in. Proceedings of the IEEE CCECE/CCGEI, Ottawa, 2006, pp. 1960-1963.
  4. X. Zheng, et al., "Perceptual shape-based natural image representation and retrieval," in Proceedings of the IEEE International Conference on Semantic Computing, 2007, pp. 622-629.
  5. Y. Mingqiang, et al., "A survey of shape feature extraction techniques," in Pattern Recognition, 2008, pp. 43-90.
  6. D. C. Tran and K. Ono, "Content-based image retrieval: Object representation by the Density of feature Points," pp. 213-218, 2000.
  7. T. Zuva, et al., "Object Shape Representation by Kernel Density Feature Points Estimator," in First International Workshop on Signal and Image Processing (SIP), Bangalore, India, 2012, pp. 209-216.
  8. E. M. Celebi and A. Y. Aslandogan, "A comparative Study of Three Moment-Based Shape Descriptors," Proceedings of the International Conference on Information Technology: Coding and Computing, 2005.
  9. T. F. Chan and L. A. Vese, "Active Contours Without Edges," IEEE, vol. 10, pp. 266-277, 2001.
  10. J. Flusser, et al., Moments and moment invariants in pattern recognition. West Sussex: John Wiley & Sons Ltd., 2009.
  11. R. Mukundan and K. R. Ramakrishnan, Moment functions in image analysis: theory and applications. Singapore: World Scientic Publishing Co. Pte. Ltd., 1998.
  12. J. S. Simonoff, "Smoothing Methods in Statistics," in Springer Series in Statistics, Springer, Ed., ed, 1996.
  13. S.-H. Cha, "Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions," International Journal of Mathematical Models and Methods in Applied Sciences, vol. 1, pp. 300-307, 2007.

Untitled Document
Untitled Document
Copyright © 2009 - 2013 - IJITCS