Abstract Employing leaf shape features for plant species recognition, especially for cultivar recognition is very challenging due to the high similarities of leaf shapes across different species and cultivars. In this paper, we attempted a new strategy of depicting leaf shapes by convolving the contour vector functions with Gaussian functions of different widths. The resulting Gaussian convolution vector (GCV) has the following traits that are desirable for shape characterization: (1) It provides an overall description to the shape in which both the curvature features and the proportional relationship of leaf contour are encoded. (2) It is intrinsically invariant to the group transformations including translation, scaling and rotation. (3) It depicts local shape geometry at multiple scales which enhance the discriminative power of the shape descriptors. The 2D histogram that reflects the distribution information of GCV is generated for efficiently yet accurately matching shapes. Two types of leaf image datasets, the middle European Woody plants (MEW2012) and the Flavia leaf dataset that are widely used for plant species recognition, and the soybean leaf dataset we built especially for cultivar recognition, are utilized to examine the effectiveness of the proposed method. The experiments demonstrate that no matter on species recognition or on cultivar recognition, the proposed method achieves higher retrieval accuracies over the state-of-the-art benchmark methods. In addition, a self-overlapped leaf image dataset is built to validate the robustness of the proposed method to self-intersection of leaves.

中文翻译：

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具有高斯卷积向量直方图的不变叶子图像识别

摘要 由于不同物种和品种的叶形高度相似，因此将叶形特征用于植物物种识别，尤其是品种识别非常具有挑战性。在本文中，我们尝试了一种通过将轮廓向量函数与不同宽度的高斯函数进行卷积来描绘叶子形状的新策略。由此产生的高斯卷积向量（GCV）具有以下特征，这些特征对于形状表征是可取的：（1）它提供了对形状的整体描述，其中曲率特征和叶轮廓的比例关系都被编码。(2) 它对包括平移、缩放和旋转在内的群变换本质上是不变的。(3) 它在多个尺度上描绘了局部形状几何，这增强了形状描述符的辨别力。生成反映 GCV 分布信息的 2D 直方图，以高效而准确地匹配形状。两种类型的叶图像数据集，广泛用于植物物种识别的中欧木本植物（MEW2012）和 Flavia 叶数据集，以及我们专门为品种识别而构建的大豆叶数据集，被用来检查所提出的有效性方法。实验表明，无论是物种识别还是品种识别，所提出的方法都比最先进的基准方法实现了更高的检索精度。此外，