COMPARATIVE ANALYSIS BETWEEN K-MEANS, FCM, AND CKMEANS ALGORITHMS FOR IMAGE SEGMENTATION

COMPARATIVE ANALYSIS BETWEEN K-MEANS, FCM, AND CKMEANS ALGORITHMS FOR IMAGE SEGMENTATION

Autores

DOI:

https://doi.org/10.17058/tecnolog.v26i1.17105

Resumo

Clustering algorithms are often used for image segmentation, aiming to group pixels by their similarity and uniformity. This process is useful to detect and highlight important areas of an image, making its analysis easier in several applications such as remote sensing and medical diagnosis. This paper have the main objective to compare the K-Means hard clustering algorithm to the FCM and ckMeans fuzzy clustering algorithms in image segmentation applications, using the R statistical programming language for analysis and visualization of the results. Uncertainty in the clustering process is discussed via the use of the alpha-cut parameter. Two experiments were conducted, using an image from an open database and an aerial image of a Catarinense city. It was found that the three methods produced similar results, when crisp clusters were considered. Fuzzy membership results of FCM and ckMeans were also compared, and it was found that, although very similar, ckMeans produced slightly lower levels of uncertainty than FCM. It was found that K-Means presents the best computational performance among the algorithms compared, which is expected due to its crisp nature. Among the fuzzy algorithms compared, ckMeans presented better performance, and FCM required less memory.

 

Downloads

Não há dados estatísticos.

Biografia do Autor

Alexandre Russini, Universidade Federal do Pampa

Professor Adjunto da Universidade Federal do Pampa, Campus Itaqui. Atua na área de Máquinas Agrícolas, Mecanização Agrícola e Agricultura de Precisão (AP)

Marcus Vinicius Pereira Saraiva, Pesquisador Assistente, Instituto de Pesquisa Econômica Aplicada - IPEA

Pesquisador Assistente, Instituto de Pesquisa Econômica Aplicada - IPEA

Rogério Rodrigues de Vargas, Universidade Federal do Pampa - Unipampa Campus Itaqui

Professor Adjunto na área de informática/programação da Universidade Federal do Pampa

Marcelo Silveira de Farias, Universidade Federal de Santa Maria

Professor Adjunto da Máquinas e Mecanização da Universidade Federal de Santa Maria -UFSM.

Catize Brandelero, Universidade Federal de Santa Maria

Professora Associada de Máquinas e Mecanização Florestal da Universidade Federal de Santa Maria -UFSM

Referências

Referências

CARVALHO, F.; BARBOSA, G.; PIMENTEL, J. Partitioning fuzzy c-means clustering algorithms for interval-valued data based on city-block distances, in 2013 brazilian conference on intelligent systems, 2013, p. 113–118. Doi: 10.1109/BRACIS.2013.27.

MACQUEEN, J. Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth berkeley symposium on mathematical statistics and probability, 1967, pp. 281–297.

BEZDEK, J.; EHRLICH, R.; FULL, W. FCM—the fuzzy c-means clustering-algorithm. Computers & Geosciences, Vol. 10, p. 191–203, 1984. Doi: 10.1016/0098-3004(84)90020-7.

FERRARO M.; GIORDANI, P., A toolbox for fuzzy clustering using the R programming language. Fuzzy Sets and Systems, Vol. 279, p. 1–16, 2015. Doi: https://doi.org/10.1016/j.fss.2015.05.001.

VARGAS, R.R.; Bedregal B, A comparative study between fuzzy c-means and ckMeans algorithms, in Annual Meeting of the North American Fuzzy Information Processing Society, Vol.1, n.1, p.1-6, 2010. DOI:10.1109/NAFIPS.2010.5548194

VARGAS, R. R.; FREDDO, R., GALAFASSI., C., GASS S.B.; RUSSINI, A., BEDREGAL B. Identifying pixels classified uncertainties ckMeansImage algorithm, in Information processing and management of uncertainty in knowledge-based systems. applications, 2018, p. 429–440.

R CORE TEAM, R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2019.

VARGAS, R.R.; DIMURO, G., BEDREGAL, B. Using ckMeans algorithm in image segmentation process: Preliminary results on mammography analysis, in Proceeding series of the brazilian society of applied and computational mathematics, Vol. 3, n.1, 2015. Doi: 10.5540/03.2015.003.01.0386.

BEZDEK, J. Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, MA, USA: Kluwer Academic Publishers, 1981.

COX, E. Fuzzy modeling and genetic algorithms for data mining and exploration. Hardcover; Elsevier/Morgan Kaufmann, 2005.

WIERMAN M. J. An introduction to the mathematics of uncertainty including set theory, logic, probability, fuzzy sets, rough sets, and evidence theory. Creighton University College of Arts; Sciences, 2010.

PONCE-CRUZ, P.; RAMÍREZ-FIGUEROA, F. D. Intelligent control systems with LabVIEWTM. Springer London, 2009.

DIMITRIADOU, E.; HORNIK K., LEISCH, F.; MEYER, D.; WEINGESSEL A. E1071: Misc functions of the department of statistics (E1071), TU Wien, R package version 1.5-24, Vol. 1. n.1, 2009.

MARTIN, D.; FOWLKES, C.; TAL, D.; MALIK, J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. 8th int’l conf. Computer vision, 2001, vol. 2, p. 416–423.

CHEN, Z.; QI, Z; MENG, F.; CUI Y.; SHI, L. Image segmentation via improving clustering algorithms with density and distance, Procedia Computer Science, Vol. 55, n.1, p. 1015–1022,2015. Doi: 10.1016/j.procs.2015.07.096.

GOOGLE EARTH. Disponível em: < https://earth.google.com/web/@0,0,0a,22251752.77375655d,35y,0h,0t,0r> Acesso em: 20 jun. 2021.

Downloads

Publicado

2022-02-03

Como Citar

Russini, A., Vinicius Pereira Saraiva, M., Rodrigues de Vargas, R., Silveira de Farias, M., & Brandelero, C. (2022). COMPARATIVE ANALYSIS BETWEEN K-MEANS, FCM, AND CKMEANS ALGORITHMS FOR IMAGE SEGMENTATION: COMPARATIVE ANALYSIS BETWEEN K-MEANS, FCM, AND CKMEANS ALGORITHMS FOR IMAGE SEGMENTATION. Tecno-Lógica, 26(1), 69-76. https://doi.org/10.17058/tecnolog.v26i1.17105

Edição

v. 26 n. 1 (2022)

Seção

Sistemas e Processos Industriais

Licença

A submissão de originais para este periódico implica na transferência, pelos autores, dos direitos de publicação impressa e digital. Os direitos autorais para os artigos publicados são do autor, com direitos do periódico sobre a primeira publicação. Os autores somente poderão utilizar os mesmos resultados em outras publicações indicando claramente este periódico como o meio da publicação original. Em virtude de sermos um periódico de acesso aberto, permite-se o uso gratuito dos artigos em aplicações educacionais e científicas desde que citada a fonte conforme a licença CC-BY da Creative Commons. Licença Creative Commons Creative Commons Atribuição 4.0 Internacional.