We present a new algorithm for image segmentation - Level-set KSVD. Level-set KSVD merges the methods of sparse dictionary learning for feature extraction and variational level-set method for image segmentation. Specifically, we use a generalization of the Chan-Vese functional with features learned by KSVD. The motivation for this model is agriculture based. Aerial images are taken in order to detect the spread of fungi in various crops. Our model is tested on such images of cotton fields. The results are compared to other methods.

翻译：我们提出了一种新的图像分割算法——Level Set KSVD。Level Set KSVD融合了用于特征提取的稀疏字典学习方法和用于图像分割的变分水平集方法。具体而言，我们使用基于KSVD学习特征的Chan-Vese泛函推广形式。该模型的提出源于农业应用背景：通过航拍图像检测不同作物中真菌的扩散情况。我们在棉花田的此类图像上对模型进行了测试，并将结果与其他方法进行了比较。