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, vol.9, no.9, 2019 (SCI-Expanded, Scopus)

Publication Type: Article / Article
Volume: 9 Issue: 9
Publication Date: 2019
Doi Number: 10.3390/app9091930
Journal Name: APPLIED SCIENCES-BASEL
Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Keywords: Texture classification, artificial bee colony programming-descriptor, image descriptor, local binary pattern, genetic programming-descriptor
Open Archive Collection: AVESIS Open Access Collection
Erciyes University Affiliated: Yes

Abstract

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.