Open AccessPlant leaf classification and retrieval using multi‐scale shape descriptor
Author(s) -
Xu Guoqing,
Li Chen
Publication year - 2021
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/tje2.12050
Subject(s) - pattern recognition (psychology) , artificial intelligence , classifier (uml) , computer science , scaling , metric (unit) , scale (ratio) , mathematics , point (geometry) , contextual image classification , image (mathematics) , cartography , operations management , geometry , economics , geography
Plant leaf classification is a significant and challenging research problem in computer vision area. In this study, an original multi‐scale shape descriptor is presented to perform leaf classification and retrieval. Firstly, a novel iterative rule is proposed as scales generation method, which is parameter free. Secondly, leaf contour points are represented by angle information which is calculated using their neighbour points under each scale. The angle information representation is invariant to image rotation, translation and scaling. More importantly, it can describe leaf in a hierarchical way by capturing leaf features from global to local variations. Then Fast Fourier Transform operation is applied to make the representation more compact and independent from starting point. Subsequently, for leaf retrieval the dissimilarity of each pair of leaf under each scale is computed using city block metric. And support vector machine is used as classifier for leaf classification. Finally, experiments and comparisons with multiple state‐of‐the‐art approaches are performed. The classification accuracy was 96.85% and 93.56% respectively on Swedish and Flavia leaf datasets. The mean average precision score was 66.42%, 76.69% and 44.14% respectively on Flavia, Swedish and MEW2012 leaf datasets. The results demonstrate that the proposed method has excellent performance.