Dendritic crystallogram images classification

Rustam A. Paringer (Login required)
Samara State Aerospace University, Russian Federation
Image Processing Systems Institute of the Russian Academy of Sciences, Samara, Russian Federation

Alexander V. Kupriyanov
Samara State Aerospace University, Russian Federation
Image Processing Systems Institute of the Russian Academy of Sciences, Samara, Russian Federation

Nataly Y. Ilyasova
Samara State Aerospace University, Russian Federation
Image Processing Systems Institute of the Russian Academy of Sciences, Samara, Russian Federation


Paper #2470 received 2015.05.30; revised manuscript received 2015.06.18; accepted for publication 2015.06.20; published online 2015.06.30.

DOI: 10.18287/jbpe-2015-1-2-135

Abstract

A computer classification system of dendritic crystallogram images is presented in this paper. To improve the quality of classification we use an algorithm for the informative features formation, using methods of discriminant analysis. The method for receiving an informativeness estimation was used. As basic features are seven geometric characteristic were calculated. The research confirming the efficiency of the formed features for classification of dendritic crystallogramms images was conducted by means of classification by support vector machine. Of these, was selected most informative basic five features and one new feature was formed. The error classification decreased from 0.081 to 0.061. The algorithm possesses a sufficient level of universality and may be applied to increase the informativeness of any feature set.

Keywords

Discriminant analysis; support vector machine; feature informativeness

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References


1. R. A. Paringer, and A. V. Kupriyanov, “Methods For Estimating Geometric Parameters of The Dendrite’s Crystallogramms,” Proceedings of 8th Open German-Russian Workshop “Pattern Recognition and Image Understanding”, 226-229 (2011).

2. R. A. Paringer, “Separation and Analysis of Dendrites on Images of Diagnostic Crystallograms of Biological Liquids,” Biomedsistems-2012, 105-108 (2012).

3. N. Y. Ilyasova, A. V. Kupriyanov, and R. A. Paringer, “Formation features for improving the quality of medical diagnosis based on the discriminant analysis methods,” Computer Optics 38(4), 851-856 (2014).

4. R. A. Paringer, and A. V. Kupriyanov, “The Method for Effective Clustering the Dendrite Crystallogram Images,” Electronic on-site Proceedings of 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW 2014) (2014).

5. R. A. Paringer, and A. V. Kupriyanov, “Research Methods for Classification of the Crystallogramms Images,” Proceedings of the 12th international conference “PRIP’2014”, 231-234 (2014).

6. K. Fukunaga, Introduction to the Image Discrimination Statistical Theory, Nauka, Moscow (1979) [in Russian].




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