Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models - Togacar, Mesut; Ergen, Burhan; Comert, Zafer (Elsevier Sci Ltd, 2020)
It is important for the sensitivity of ecological balance that image processing methods and techniques give better results day by day. Today, researchers use deep learning in image-based object recognition. Recently, the use of deep learning methods on plant species has increased. In this study, a hybrid method that is used together with feature selection methods and Convolutional Neural Network (CNN) models is presented. In the proposed model, CNN models are used for feature extraction. The features obtained from these models are combined and efficient features are selected with feature selection methods. The aim here is to subtract and classify intersecting features between the features obtained by feature selection methods. When the results of the experiments are compared, the intersection of the features obtained by feature selection methods are contributed to the classification performance. The classification success achieved by the Support Vector Machine (SVM) method was 98.91%. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords: Image processing; Deep learning; Flower species; Feature selection; Feature intersection