Abstract
K-singular value decomposition (K-SVD) is a frequently used dictionary learning (DL) algorithm that iteratively works between sparse coding and dictionary updating. The sparse coding process generates sparse coefficients for each training sample, and the sparse coefficients induce clustering features. In the applications like image processing, the features of different clusters vary dramatically. However, all the atoms of dictionary jointly represent the features, regardless of clusters. This would reduce the accuracy of sparse representation. To address this problem, in this study, we develop the clustering K-SVD (CK-SVD) algorithm for DL and the corresponding greedy algorithm for sparse representation. The atoms are divided into a set of groups, and each group of atoms is employed to represent the image features of a specific cluster. Hence, the features of all clusters can be utilized and the number of redundant atoms are reduced. Additionally, two practical extensions of the CK-SVD are provided. Experimental results demonstrate that the proposed methods could provide more accurate sparse representation of images, compared to the conventional K-SVD and its existing extended methods. The proposed clustering DL model also has the potential to be applied to the online DL cases.
Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning