Efficient Techniques for a Very Accurate Measurement of Dissimilarities between Cyclic Patterns
Two efficient approximate techniques for measuring dissimilarities between cyclic patterns are presented. They are inspired on the quadratic time algorithm proposed by Bunke and Bühler. The first technique completes pseudoalignments built by the Bunke and Bühler algorithm (BBA), obtaining full alignments between cyclic patterns. The edit cost of the minimum-cost alignment is given as an upper-bound estimation of the exact cyclic edit distance, which results in a more accurate bound than the lower one obtained by BBA. The second technique uses both bounds to compute a weighted average, achieving even more accurate solutions. Weights come from minimizing the sum of squared relative errors with respect to exact distance values on a training set of string pairs. Experiments were conducted on both artificial and real data, to demonstrate the capabilities of new techniques in both accurateness and quadratic computing time.
KeywordsCyclic patterns cyclic strings approximate string matching structural pattern analysis 2D shape recognition
- H. Bunke and A. Sanfeliu, editors. Syntactic and Structural Pattern Recognition Theory and Applications, Singapore, 1990. World Scientific.Google Scholar
- M. Maes. On a cyclic string-to-string correction problem. Information Processing Letters, 35(2):73–78, June 1990.Google Scholar
- J. Gregor and M. G. Thomason. Dynamic programming alignment of sequences representing cyclic patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(2):129–135, February 1993.Google Scholar
- G. Andreu, A. Crespo, and J. M. Valiente. Selecting the toroidal self-organizing feature maps (TSOFM) best organized to object recognition. In Proceedings of ICNN 97, volume 2, pages 1341–1346, Houston, Texas (USA), June 1997. IEEE.Google Scholar
- R. C. Gonzalez and R. E. Woods. Digital Image Processing. Addison-Wesley, 1992.Google Scholar