Harmonic Mean Projection Shape Transform for Leaf Classification

Shape feature extraction has emerged as an important part of computer vision and image processing applications. Identification of shape objects in huge data based on shape similarity still remains a challenging problem due to the similarity of the shapes and existing of classes with similar contour information. This paper presents to investigate the shape analysis for plant leaf classification by provide a suitable technique in feature extraction, a new approach Harmonic mean projecting transform which is adapted from the Radon transform is proposed in this study to extract the shape information in order to classify the leaf images into different classes and this paper propose a framework for the application of leaf classification. The process considers all the pixels’ information using the harmonic mean formula and enhanced with similarity measure methods called DIMI in the feature extraction process, these two engages techniques are proposed to meet rotation and scale invariant transformation. Encouraging experimental results on Swedish leaf dataset demonstrated that the proposed method can achieve better accuracy compared to the state-of-the-art techniques using precision, recall and accuracy standard evaluation metrics.