A Novel Texture Classification Procedure by using Association Rules

L. Jaba Sheela, V. Shanthi

Abstract


Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. Association rules have been used in various applications during the past decades. Association rules capture both structural and statistical information, and automatically identify the structures that occur most frequently and relationships that have significant discriminative power. So, association rules can be adapted to capture frequently occurring local structures in textures. This paper describes the usage of association rules for texture classification problem. The performed experimental studies show the effectiveness of the association rules. The overall success rate is about 98%.

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References


Sklansky J., Image segmentation and feature extraction, IEEE Trans. System Man Cybernat., 8,237-247, 1978.

Arivazhagan S. and Ganesan L., Texture classification using wavelet transform, Pattern Recog. Lett. 24, 1513-1521, 2003.

Weszka J. S., Dyer C.R., Rosenfeld A, A comparative study of texture measures for terrain classification, IEEE Trans. Syst. Man. Cybernet. 6, 269-285,1976.

Jain A.K., Farrokhnia F., Unsupervised texture segmentation using Gabor filters, Patt. Recog., 24, 1167-1186, 1991.

Muneeswaran K., Ganesan L., Arumugam S. and Ruba Soundar K., Texture classification with combined rotation and scale invariant wavelet features, Pattern Recognition, 38, 10, 1495-1506, 2005

Chen C. C., Chen C. C. Filtering methods for texture discrimination, Patt. Rec. Lett. 20, 783-790, 1999.

Rushing, J.A.; Ranganath, H.S.; Hinke, T.H.; Graves, S.J., Using association rules as texture features, Pattern Analysis and Machine Intelligence,IEEE Transactions on Vol. 23, 845 – 858, Aug. 2001

Agrawal R., Imielinski T., and Swami A., Mining association rules between sets of items in large databases, In proc. ACM SIGMOD international conf. on Management of data, Washington DC, May 1993.

Agrawal R., Srikant R., Fast algorithms for mining association rules in large databases, In proc. 20th Int. Conf. on very large databases, pp. 487-499. Santiago, Chile, 1994.

http://sipi.usc.edu/database/database.cgi?volume=texturesℑ=1#top




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.2008.2.2.2

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