Improved clustering criterion for image clustering with artificial bee colony algorithm


OZTURK C., Hancer E., KARABOĞA D.

PATTERN ANALYSIS AND APPLICATIONS, cilt.18, sa.3, ss.587-599, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 3
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1007/s10044-014-0365-y
  • Dergi Adı: PATTERN ANALYSIS AND APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.587-599
  • Anahtar Kelimeler: Image clustering, Artificial bee colony algorithm, Genetic algorithms, K-means, Particle swarm optimization, Validity indexes, PARTICLE SWARM OPTIMIZATION, NETWORKS
  • Erciyes Üniversitesi Adresli: Evet

Özet

In this paper, a new objective function is proposed for image clustering and is applied with the artificial bee colony (ABC) algorithm, the particle swarm optimization algorithm and the genetic algorithm. The performance of the proposed objective function is tested on seven benchmark images by comparing it with the three well-known objective functions in the literature and the K-means algorithm in terms of separateness and compactness which are the main criterions of the clustering problem. Moreover, the Davies-Bouldin Index and the XB Index are also employed to compare the quality of the proposed objective function with the other objective functions. The simulated results show that the ABC-based image clustering method with the improved objective function obtains well-distinguished clusters.