HKC: A Dictionary Training Algorithm for Sparse Representation

摘要：

In recent years, research on dictionary design for sparse representation(SR) has changed from pre-defined to training. A Hierarchical K-means Clustering (HKC) dictionary training algorithm is proposed in this paper. The algorithm presents a framework for SR for a class of images. HKC used Kmeans clustering to generate atoms which is one to one corresponding to hyperplanes for approximating hyperspherical cap. Compared with conventional algorithms, this algorithm is more flexible and efficiency. Finally, experimental results show that this algorithm can be used for compressive sensing and denoising.

关键词： sparse representation compressive sensing atom dictionary

作者: Jian Xu Zhiguo Chang

作者单位: The School of Communication and Information Engineering Xi’an University of Posts & Telecommunicatio School of Information Engineering Chang’an University Xi’an China

会议类型: 国际会议

会议名称: 2010年IEEE多媒体信息网络与安全国际会议

会议地点: 南京

会议语种:英文

页码: 54-57

在线出版日期: 2010-11-01（万方平台首次上网日期，不代表论文的发表时间）