  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.
