Evaluation of SENSC Algorithm for Image Clustering
SENSC algorithm is a newly proposed stable and efficient NSC algorithm. In this paper the SENSC algorithm is evaluated for the task of image clustering. A series of experiments are conducted on two different kinds of image datasets, including face images and natural images, and SENSC is compared with some other commonly used clustering methods. Experimental results show that SENSC is better suited for the clustering of non-negative, well structured data which lies in some clear, meaningful underlying low-dimensional subspace.
Yinfeng Qin Le Li Yu-Jin Zhang
Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
266-271
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）