Unsupervised image segmentation (UIS) is a very challenging problem in computer vision and artificial intelligence. In general, the UIS can be viewed as a clustering problem, which aims to partition the image into K sets of regions that have coherent color and texture features. Clearly, the UIS results depend on the employed clustering methods. Inspired by the recent rapid progress of $l1-norm$ minimization techniques and the great success of $l1-norm$ minimization based sparse representation (SR), in this paper we propose a K-sparse clustering algorithm to segment the image into K partitions. The given...