A sparsity augmented probabilistic collaborative representation based classification method. (July 2020)

• Record Type: Journal Article
• Authors: Cai, Xiao-Yun; Yin, He-Feng
• Is Part Of: Journal of algorithms & computational technology. Volume 14(2020)
• Publication Date: 2020-07
• Subjects: Image recognition -- probabilistic collaborative representation based classification -- sparse representation -- sparsity augmented; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302620931042
• Languages: English
• ISSNs: 1748-3018

Abstract

In order to enhance the performance of image recognition, a sparsity augmented probabilistic collaborative representation based classification (SA-ProCRC) method is presented. The proposed method obtains the dense coefficient through ProCRC, then augments the dense coefficient with a sparse one, and the sparse coefficient is attained by the orthogonal matching pursuit (OMP) algorithm. In contrast to conventional methods which require explicit computation of the reconstruction residuals for each class, the proposed method employs the augmented coefficient and the label matrix of the training samples to classify the test sample. Experimental results indicate that the proposed method can achieve promising results for face and scene images. The source code of our proposed SA-ProCRC is accessible athttps://github.com/yinhefeng/SAProCRC