Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{arslan:2019:AS,

• author = "Sibel Arslan and Celal Ozturk",
• title = "Artificial Bee Colony Programming Descriptor for {Multi-Class} Texture Classification",
• journal = "Applied Sciences",
• year = "2019",
• volume = "9",
• number = "9",
• note = "Special Issue Machine Learning and Compressed Sensing in Image Reconstruction",
• keywords = "genetic algorithms, genetic programming, Texture classification, artificial bee colony programming-descriptor, image descriptor, local binary pattern, genetic programming-descriptor",
• ISSN = "2076-3417",
• URL = "https://www.mdpi.com/2076-3417/9/9/1930",
• URL = "https://www.mdpi.com/2076-3417/9/9/1930.pdf",
• DOI = "doi:10.3390/app9091930",
• size = "18 page",
• abstract = "Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behaviour of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.",
• notes = "also known as \cite{app9091930}",

}

Genetic Programming entries for Sibel Arslan Celal Ozturk

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