Fast Plant Leaf Recognition Using Improved Multiscale Triangle Representation and KNN for Optimization

Abstract

Due to the complexity and similarity of plant leaves, it is very important to study an effective leaf-feature extraction method to improve the recognition rate of plant leaves. We study five multi-scale triangle representations: the triangle unsigned area representation (TUA), the triangle vertex angle representation (TVA) and three new representations, which we define as the gray average (TGA), the gray standard deviation (TGSD) and the side length integral (TSLI) on the triangle. In this method the curvature features of the contour, the texture features and the shape area feature are extracted to provide a multiscale leaf-feature description, and a new adaptive KNN for optimization method is proposed to improve the retrieval rate of leaf datasets. Experiments show that compared with the state-of-the-art methods, our method has higher accuracy on the Swedish and Flavia plant leaf datasets, which are respectively 99.35% and 99.43% with 84.76% Mean Average Precision (MAP) value and has comparable results on MPEG-7, kimia99 and kimia216 datasets. When our method is combined with KNN for optimization, the retrieval rate of the above datasets has been significantly improved, especially MAP on the Flavia dataset increases to 94.48%.