Methods of Semantic Integrity Preservation in the Pattern Recognition Process
| Autor: | Anastasiia Matveeva, Ilya I. Viksnin, Roman O. Patrikeev, Iuliia Kim |
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| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
General Computer Science Process (engineering) business.industry Computer science 05 social sciences 02 engineering and technology computer.software_genre 0502 economics and business Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Semantic integrity 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | International Journal of Embedded and Real-Time Communication Systems. 10:118-140 |
| ISSN: | 1947-3184 1947-3176 |
| DOI: | 10.4018/ijertcs.2019070108 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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