Color Image Quantization: A Short Review and an Application with Artificial Bee Colony Algorithm

Creative Commons License

ÖZTÜRK C., Hancer E., KARABOĞA D.

INFORMATICA, cilt.25, sa.3, ss.485-503, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 25 Sayı: 3
  • Basım Tarihi: 2014
  • Doi Numarası: 10.15388/informatica.2014.25
  • Dergi Adı: INFORMATICA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.485-503
  • Anahtar Kelimeler: color quantization, artificial bee colony, particle swarm optimization, K-means, fuzzy C means, MEANS CLUSTERING-ALGORITHM, NEURAL-NETWORKS, OPTIMIZATION
  • Erciyes Üniversitesi Adresli: Evet

Özet

Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others.