Generalized ICM for image segmentation based on Tsallis statistics

KILIÇ İ., Kayacan O.

Physica A: Statistical Mechanics and its Applications, cilt.391, sa.20, ss.4899-4908, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 391 Sayı: 20
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.physa.2011.12.062
  • Dergi Adı: Physica A: Statistical Mechanics and its Applications
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.4899-4908
  • Anahtar Kelimeler: Tsallis entropy, Image segmentation, Markov random field, Iterated conditional modes
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

In this paper, the iterated conditional modes optimization method of a Markov random field technique for image segmentation is generalized based on Tsallis statistics. It is observed that, for some q entropic index values the new algorithm performs better segmentation than the classical one. The proposed algorithm also does not have a local minimum problem and reaches a global minimum energy point although the number of iterations remains the same as ICM. Based on the findings of the new algorithm, it can be expressed that the new technique can be used for the image segmentation processes in which the objects are Gaussian or nearly Gaussian distributed. © 2012 Elsevier B.V. All rights reserved.