Advances in Mathematical Physics
Volume 2013 (2013), Article ID 950289, 8 pages
Research Article

Extraction of Affine Invariant Features Using Fractal

1School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
2School of Information Science and Technology, East China Normal University, No. 500, Dong-Chuan Road, Shanghai 200241, China

Received 19 March 2013; Accepted 29 April 2013

Academic Editor: Chen Wensheng

Copyright © 2013 Jianwei Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An approach based on fractal is presented for extracting affine invariant features. Central projection transformation is employed to reduce the dimensionality of the original input pattern, and general contour (GC) of the pattern is derived. Affine invariant features cannot be extracted from GC directly due to shearing. To address this problem, a group of curves (which are called shift curves) are constructed from the obtained GC. Fractal dimensions of these curves can readily be computed and constitute a new feature vector for the original pattern. The derived feature vector is used in question for pattern recognition. Several experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method can be used for object classification.