Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Sibel Arslan,Celal Ozturk

doi:10.3390/app9091930

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.

Highlights

The image descriptor provides information about the image by extracting / determining features such as shape or color
When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to Local Binary Pattern (LBP) and GP-descriptor
Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF) are examples of instances in which sparse descriptors evaluate each pixel in the image and extract the models [1]

Summary

Introduction

The image descriptor provides information about the image by extracting / determining features such as shape or color. One of the most common dense descriptors is the Local Binary Pattern (LBP). Operator assigns binary values as a result of comparing them to each other by scrolling floating is one of the most common methods in image processing. Operator assigns binary values as a result of comparing them to each other by scrolling floating windows and selecting center value the middle ofof these windows level. Operator assigns binarythe values aspixel apixel result ofincomparing them to each otheras by scrolling floating windows and selecting the center value in the middle these windows asthe thethreshold threshold level. The generated binary number sequence is called the code, which is used to specify different windows and selecting the center pixel value in the middle of these windows as the threshold level.

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Conclusion