Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

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 behavior 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.

A Comparative Study of Automatic Programming Techniques

“…When a better source of any source is discovered, that source's penalty point is reset. Once all the employed bees and onlooker bees have completed the search operations in each cycle, the penalty points of sources are checked [42].…”

HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

“…One of the most effective meta-heuristic algorithms is an Artificial Bee Colony (ABC) algorithm [19], which has been applied successfully to various optimization problems. Several studies have documented the performance of the ABC algorithm on optimization problems [20][21][22][23]. However, ABC algorithm has suffered from problems with convergence speed, simple operation of solution development and weak information sharing activity.…”