A sparsity augmented probabilistic collaborative representation based classification method

TL;DR

This paper introduces SA-ProCRC, a novel image classification method that combines dense and sparse coefficients for improved recognition accuracy, demonstrated on face and scene image datasets.

Contribution

The paper proposes a sparsity augmented probabilistic collaborative representation method that enhances classification performance by integrating dense and sparse coefficients.

Findings

Achieves promising results on face and scene image datasets

Outperforms some conventional classification methods

Provides accessible source code for reproducibility

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 at https://github.com/yinhefeng/SAProCRC.