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Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Iuliia Kim, Anastasiia Matveeva, Ilya Viksnin, Roman Patrikeev
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 23
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781522571988|DOI: 10.4018/IJERTCS.2019070108
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MLA

Kim, Iuliia, et al. "Methods of Semantic Integrity Preservation in the Pattern Recognition Process." IJERTCS vol.10, no.3 2019: pp.118-140. http://doi.org/10.4018/IJERTCS.2019070108

APA

Kim, I., Matveeva, A., Viksnin, I., & Patrikeev, R. (2019). Methods of Semantic Integrity Preservation in the Pattern Recognition Process. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 10(3), 118-140. http://doi.org/10.4018/IJERTCS.2019070108

Chicago

Kim, Iuliia, et al. "Methods of Semantic Integrity Preservation in the Pattern Recognition Process," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 10, no.3: 118-140. http://doi.org/10.4018/IJERTCS.2019070108

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Abstract

In this article, much attention is paid to pattern recognition quality, especially the visual information semantic integrity preservation. The main purpose is to find the ways of its possible improvement to the three basic stages of the pattern recognition process: image preparation, image processing, and classification. To avoid semantic integrity violations of information, in the initial stage of the image analysis, normalization is proposed. In the second stage, a new clustering method was developed, based on particle swarm optimization and the k-means algorithm. In the final stage of the pattern recognition process the Haar classifier was used with normalized training samples. The proposed algorithm and only Haar classifier with non-normalized samples were tested on 500 blurred images: in 8% of samples both algorithms provided semantic integrity preservation and in 64% only the developed algorithm worked effectively.

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