Classification of Flowers Using Neural Networks Approach

Abstract

This article proposed a technique for the classification of flowers based on texture and shape features using Feed-forward Neural Networks. This paper provides a unique solution for classifying flowers based on their texture, and geometrical value. The proposed method has three steps: (i) Segmentation; (ii) Masking; and (iii) classification. The segmentation was achieved by a familiar existing method called structural matrix decomposition (SMD) and considered two types of features called, GLCM (Gray-level co-occurrence matrix) and Shape features. In this proposed work, we adapted the Feed-forward NN algorithm for the classification of seven varieties of flower images. Experimentation was conducted using a dataset of 270 images of 7 classes to demonstrate the proposed model's performance. The experiment results demonstrate that a combination of GLCM, Hue-GLCM and Geometrical features gives a 96.90 % of accuracy rate. An experimental result shows the efficiency of the proposed approach.