IEEE/ACM Trans Comput Biol Bioinform. 2023 Jan-Feb;20(1):39-51. doi: 10.1109/TCBB.2022.3148463. Epub 2023 Feb 3.
Plant identification based on leaf images is a widely concerned application field in artificial intelligence and botany. The key problem is extracting robust discriminative features from leaf images and assigning a measure of similarity. This study proposes an effective, robust shape descriptor to identify plant species from images of their leaves, which we call the high-level triangle shape descriptor (HTSD). First, we extract a leaf image's external contour and internal salient point information. We then use triangle features to describe the leaf contour, which we call the contour point based on triangle features (CPTFs). The internal information of the leaf image is based on salient point triangle features (SPTFs). The third step is to apply the Fisher vector to encode the two kinds of point-based local triangle features into the HTSD. Finally, we employ the simple euclidean distance to calculate the dissimilarities between the HTSD characteristics of leaf images. We have extensively evaluated the proposed approach on several public leaf datasets successfully. Experimental results show that our method has superior recognition accuracy, outperforming current state-of-the-art shape-based and deep-learning plant identification approaches.
基于叶片图像的植物识别是人工智能和植物学中一个备受关注的应用领域。关键问题是从叶片图像中提取鲁棒的鉴别特征并分配相似性度量。本研究提出了一种有效的、鲁棒的形状描述符，用于从叶片图像中识别植物物种，我们称之为高级三角形形状描述符（HTSD）。首先，我们提取叶片图像的外部轮廓和内部显著点信息。然后，我们使用三角形特征来描述叶片轮廓，我们称之为基于三角形特征的轮廓点（CPTFs）。叶片图像的内部信息基于显著点三角形特征（SPTFs）。第三步是应用 Fisher 向量将两种基于点的局部三角形特征编码为 HTSD。最后，我们采用简单的欧几里得距离来计算叶片图像 HTSD 特征之间的差异。我们已经在几个公开的叶片数据集上成功地对所提出的方法进行了广泛的评估。实验结果表明，我们的方法具有更高的识别精度，优于当前最先进的基于形状和深度学习的植物识别方法。
