A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)

Abstract

The traditional karyotype studies are widely used in plant systematics to evaluate the positions of species. However, studies sometimes can not solve systematic problems. In recent years, computer-based systems have gained importance in contributing to the solution of the taxonomical problems. The aim of this study was to identify three Lallemantia species by using the LBP (local binary pattern) texture operator based on chromosome images in mitosis. LBP is the one of the most powerful and easily applicable tool for identifying local image patterns. In this study, microphotographs of 641 cells in the metaphase stage of mitosis were used. The LBP involves preprocessing, feature extraction, feature selection, and classification. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), K-nearest neighbor (KNN), bagged tree (BT) and ensemble subspace nearest neighbor (SNN) were used in the classification stage. This study found that the best acting classifier was SNN because achievement rate was one hundred percent. Also, a dendrogram was formed to measure the similarity among the three species. As a result, LBP can be accepted as a tool for classifying plants by using chromosome images.