Plant leaf segmentation has a very important role in most plant identification methods. Tree leaves segmentation in images with complex background is very difficult when there is no prior information about the leaves and backgrounds. In practice, the parameters of unsupervised image segmentation algorithms must be set for each image to get the best results. In this paper, to overcome this problem, fusion of the results of five leaf segmentation algorithms (fuzzy c-means, SOM and k-means in various color spaces or different parameters) is applied. To fuse the results of these segmentations, new equations for mutual information (g-mutual information equations) based on the g-calculus are introduced to find the best consensus segmentation. The results of the mentioned primary clustering algorithms are considered as a new feature vector for each pixel. To reduce the time complexity, a fast method is employed using truth table containing different feature vectors. To evaluate this new approach, a leaf image database with natural scenes, taken from Pl@ntLeaves database, is generated to have different positions and orientations. In addition, a widely used database is used to compare the proposed method with other methods. The experimental results presented in this paper show that the use of g-calculus in fusion of image segmentations improves the evaluation parameters.

在大多数植物鉴定方法中，植物叶片分割具有非常重要的作用。当没有关于树叶和背景的先验信息时，很难对具有复杂背景的图像进行树叶分割。实际上，必须为每个图像设置无监督图像分割算法的参数，以获得最佳结果。在本文中，为克服此问题，对五种叶子分割算法（模糊c均值，SOM和k均值在各种颜色空间或不同参数中）的结果进行了融合。为了融合这些分割，新方程互信息的结果（摹基础上，-mutual信息等式）g ^-演算被引入以找到最佳的共识分割。提到的主要聚类算法的结果被视为每个像素的新特征向量。为了减少时间复杂度，使用包含不同特征向量的真值表的快速方法。为了评估这种新方法，从Pl @ ntLeaves数据库获取具有自然场景的叶子图像数据库，使其具有不同的位置和方向。另外，使用广泛使用的数据库将提出的方法与其他方法进行比较。本文提出的实验结果表明，在图像分割融合中使用g-演算可以改善评估参数。