52 Unsupervised Image Segmentation via Minimization Learning
-
Published:2012
Download citation file:
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 minimization techniques and the great success of minimization based sparse representation (SR), in this paper we propose a K-sparse clustering algorithm to segment the image into K partitions. The given image is first over-segmented into many small regions, from which the K clustering centers are learned under the SR framework using minimization. Finally, each of the regions can be classified into one of the K classes based on its SR coefficients over the K cluster centers. The experimental results on the benchmark images demonstrate the effectiveness and superiority of the proposed K-sparse clustering method to the classical K-means clustering method and recently developed sparse subspace clustering method in applications of UIS.