Plant identification using leaf shapes—A pattern counting approach

• Doing statistics over learned shape patterns, rather than matching contour points.
• The proposed method can classify both simple leaves and compound leaves.
• The learned shape patterns measure leaf shapes from different aspects.
• We do NOT require any pre-processing of the leaf contours, i.e. petioles removal.
• Experiments on public datasets show the effectiveness of the proposed method.

Plant identification is required by all walks of life, from professionals to the general public. Nevertheless, it is not an easy job but requires specialized knowledge. In this paper, we propose a new method for plant identification using shapes of their leaves. Different from existing studies which target at simple leaves, the proposed method can accurately recognize both simple and compound leaves. In specifics, we propose a novel feature that captures global and local shape information independently so that they can be examined individually during classification. Furthermore, we advocate that when comparing two leaf individuals it is better to “count” the number of certain shape patterns rather than to match the extracted shape features in a point-wise manner. The proposed counting-based shape descriptor is not only discriminative for classification but also computationally fast and storage cheap. Experiments conducted on five leaf image datasets demonstrate that our algorithm significantly outperforms the state-of-the-art methods in terms of recognition accuracy, efficiency and storage requirement.