Skip to main content
Erschienen in: Soft Computing 2/2010

01.01.2010 | Focus

An automatic fuzzy c-means algorithm for image segmentation

verfasst von: Yan-ling Li, Yi Shen

Erschienen in: Soft Computing | Ausgabe 2/2010

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Ball G, Hall D (1967) A clustering technique for summarizing multivariate data. Behav Sci 12:153–155CrossRef Ball G, Hall D (1967) A clustering technique for summarizing multivariate data. Behav Sci 12:153–155CrossRef
Zurück zum Zitat Bezdek JC (1975) Mathematical models for systematic and taxonomy. In: Proceedings of eight international conference on numerical taxonomy, San Francisco, pp 143–166 Bezdek JC (1975) Mathematical models for systematic and taxonomy. In: Proceedings of eight international conference on numerical taxonomy, San Francisco, pp 143–166
Zurück zum Zitat Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkMATH Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkMATH
Zurück zum Zitat Dunn JC (1974) A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters. J Cybern 3:32–57CrossRefMathSciNet Dunn JC (1974) A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters. J Cybern 3:32–57CrossRefMathSciNet
Zurück zum Zitat Hall LO, Kanade PM (2005) Swarm based fuzzy clustering with partition validity. Proc. 14th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE’05). IEEE Press, Piscataway, NJ. 991–995 Hall LO, Kanade PM (2005) Swarm based fuzzy clustering with partition validity. Proc. 14th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE’05). IEEE Press, Piscataway, NJ. 991–995
Zurück zum Zitat Hall LO, Kanade PM (2007) Fuzzy ants and clustering. IEEE Trans Syst Man Cybern A Syst Hum 37(5):758–769CrossRef Hall LO, Kanade PM (2007) Fuzzy ants and clustering. IEEE Trans Syst Man Cybern A Syst Hum 37(5):758–769CrossRef
Zurück zum Zitat Huang GR, Wang XF, Cao XB (2006) Ant colony optimization algorithm based on directional pheromone diffusion. Chin J Electron 15(3):447–450 Huang GR, Wang XF, Cao XB (2006) Ant colony optimization algorithm based on directional pheromone diffusion. Chin J Electron 15(3):447–450
Zurück zum Zitat Kim DW, Lee KH, Lee D (2004) On cluster validity index for estimation of optimal number of fuzzy clusters. Pattern Recognit 37:2009–2024 Kim DW, Lee KH, Lee D (2004) On cluster validity index for estimation of optimal number of fuzzy clusters. Pattern Recognit 37:2009–2024
Zurück zum Zitat Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst 3(3):370–379CrossRef Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst 3(3):370–379CrossRef
Zurück zum Zitat Pham DL, Prince JL (1999) An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognit Lett 20:57–68MATHCrossRef Pham DL, Prince JL (1999) An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognit Lett 20:57–68MATHCrossRef
Zurück zum Zitat Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithm and applications in image segmentation. Graph Models Image Process 58(3):246–261CrossRef Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithm and applications in image segmentation. Graph Models Image Process 58(3):246–261CrossRef
Zurück zum Zitat Shelokar PS, Jayaraman VK, Kulkami BD.(2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195CrossRef Shelokar PS, Jayaraman VK, Kulkami BD.(2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195CrossRef
Zurück zum Zitat Tou JT, Gonzalez RC (1974) Pattern recognition principles. Addison-Wesley, LondonMATH Tou JT, Gonzalez RC (1974) Pattern recognition principles. Addison-Wesley, LondonMATH
Zurück zum Zitat Wallace CS, Boulton DM (1968) An information measure for classification. Comput J 11(2):185–194MATH Wallace CS, Boulton DM (1968) An information measure for classification. Comput J 11(2):185–194MATH
Zurück zum Zitat Xie XL, Beni GA (1991) Validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3:841–846CrossRef Xie XL, Beni GA (1991) Validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3:841–846CrossRef
Zurück zum Zitat Yamany SM, Farag AA, Hsu S (1999) A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems. Pattern Recognit Lett 20:1431–1438CrossRef Yamany SM, Farag AA, Hsu S (1999) A fuzzy hyperspectral classifier for automatic target recognition (ATR) systems. Pattern Recognit Lett 20:1431–1438CrossRef
Metadaten
Titel
An automatic fuzzy c-means algorithm for image segmentation
verfasst von
Yan-ling Li
Yi Shen
Publikationsdatum
01.01.2010
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 2/2010
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-009-0442-0

Weitere Artikel der Ausgabe 2/2010

Soft Computing 2/2010 Zur Ausgabe