基于多示例图的小麦叶部病害分割方法

图像分割是作物病害自动识别的难点之一,传统的基于阈值或聚类的分割方法分割精度较低,为了提高作物病害的分割效果,该文提出了一种基于多示例图的分割模型,将作物病害的分割问题转化为在多示例框架下图的分割问题,同时在对作物病害图像分割的过程中引入空间信息,采用像素点信息和邻域信息的融合值形成特征空间,通过在包空间的有效度量方式将多示例学习与图的分割方法有效结合进行小麦叶部病害图像的分割,从而更好地度量示例包的内部差异和示例包之间的差异,同时兼顾了图像的局部信息和全局信息,通过对小麦锈病和白斑病图像的分割试验表明,所提出的模型具有较好的鲁棒性,并且分割效果明显高于传统的分割方法。

Multi-instance graph approach to wheat leaf disease segmentation

Image segmentation is one of the difficult issues in crop disease recognition. Traditional methods based on threshold or clustering are mostly used in present research, but the precision is low. A multi-instance graph model was proposed in this paper to improve the segmentation performance of crop disease. The image segmentation was formulated as a graph segmentation problem under multi-instance learning framework, where the neighborhood information of pixels was fused to build the feature space and the adaptive geometric relationship between two packages of instances was introduced to wheat leaf disease segmentation, which was combined with the global and local features and had better measurement of interior difference and exterior difference. The experimental results show that this approach outperforms other methods and is effective for wheat leaf disease segmentation.