Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Arslan, Sibel; ÖZTÜRK , CELAL

doi:10.3390/app9091930

Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Arslan S., ÖZTÜRK C.

APPLIED SCIENCES-BASEL, cilt.9, sa.9, 2019 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 9 Sayı: 9
Basım Tarihi: 2019
Doi Numarası: 10.3390/app9091930
Dergi Adı: APPLIED SCIENCES-BASEL
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Anahtar Kelimeler: Texture classification, artificial bee colony programming-descriptor, image descriptor, local binary pattern, genetic programming-descriptor
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
Erciyes Üniversitesi Adresli: Evet

Özet

Featured Application Texture classification aims to identify textures using few samples. Local Binary Pattern (LBP) and GP-descriptor are most used texture classification algorithms. Artificial Bee Colony Programming-Descriptor (ABCP-Descriptor) evaluates samples to extract mathematical models. Comparative results show that proposed ABCP-Descriptor is a successful texture classification method.