Issue:Intuitionistic fuzzy negations and their use in image classification

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Title of paper: Intuitionistic fuzzy negations and their use in image classification
Author(s):
Alžbeta Michalíková
Faculty of Natural Sciences, Matej Bel University, Tajovskeho 40, Banska Bystrica, Slovakia
Mathematical Institute, Slovak Academy of Sciences, Dumbierska 1, Banska Bystrica, Slovakia
alzbeta.michalikovaAt sign.pngumb.sk
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 26 (2020), Number 3, pages 22–32
DOI: https://doi.org/10.7546/nifs.2020.26.3.22-32
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Abstract: In this paper, the problem of classification of images is discussed. Our specific problem is that we need to classify tire images into selected classes. The classes are characterized by some patterns. In the first step images are represented as the vectors. Then the membership and non-membership value to each coordinate of the vector is calculated and the theory of intuitionistic fuzzy sets is used. In [7] the classification of images was performed with respect to the valued of so called Sim function, which was defined as a ratio of distance between pattern data and image data and distance between pattern data and the complement of image data. The complement of image data was obtained by using specific intuitionistic fuzzy negation. In [2] a list of 53 intuitionistic fuzzy negations was presented. We have decided to use some of these negations to improve the results of classification.
Keywords: Intuitionistic fuzzy sets, Intuitionistic fuzzy negations, Similarity measure, Image classification.
AMS Classification: 03C98, 03E72.
References:
  1. Atanassov, K. T. (1983). Intuitionistic fuzzy sets, VII ITKR Session, Sofia, 20-23 June 1983 (Deposed in Centr. Sci.-Techn. Library of the Bulg. Acad. of Sci., 1697/84) (in Bulgarian). Reprinted: Int. J. Bioautomation, 2016, 20 (S1), S1–S6.
  2. Atanassov, K. T. (2017). Intuitionistic Fuzzy Logics, Springer International Publishing AG 2017, Switzerland. DOI 10.1007/978-3-319-48953-7.
  3. Bodziak, W. J. (2017). Forensic Footwear Evidence, CRC Press.
  4. Intarapaiboon, P. (2016). Text classification using similarity measures on intuitionistic fuzzy sets. SCIENCEASIA, 42 (1), 52–60.
  5. Lux, F. H. (2013). Tire Track Identification, Journal of Forensic Research, Vol. 4:198. doi:10.4172/2157-7145.1000198.
  6. Michalíková, A. (2019). Intuitionistic fuzzy sets and their use in image classification, Notes on Intuitionistic Fuzzy Sets, 25 (2), 60–66.
  7. Michalíková, A. (2019). Classification of images by using distance functions defined on intuitionistic fuzzy Sets, in Advances in Intelligence Systems and Computing. Warsaw, Poland. Springer. Submitted.
  8. Szmidt, E., & Kacprzyk, J. (2009). Analysis of Similarity Measures for Atanassov’s Intuitionistic Fuzzy Sets, in IFSA/EUSFLAT Conference, pp. 1416–1421.
  9. Vagac, M., Melichercik, M., Marko, M., Trhan, P., Michal´ıkova, A., Kliment R. & Drapka, R. (2015). Crawling Images with Web Browser Support. 13th International IEEE Scientific Conference on Informatics’2015, 286–289.
  10. Vagac, M., Melichercik, M. & Schon, J. (2015). Classification of Tire Images in Order to Obtain the Best Possible Tire Tread Sample. The 5th International Scientific Conference, Applied Natural Science 2015. September 30–October 2, 2015, Jasna; Trnava : UCM, 173.
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