A sparsity augmented probabilistic collaborative representation based classification method

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...

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Bibliographic Details
Published in:Journal of algorithms & computational technology 2020, Vol.14
Main Authors: Cai, Xiao-Yun, Yin, He-Feng
Format: Article
Language:English
Subjects:
Algorithms
Coefficients
Collaboration
Image classification
Image enhancement
Image reconstruction
Matched pursuit
Object recognition
Representations
Source code
Sparsity
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Summary: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
ISSN:1748-3026
1748-3018
1748-3026
DOI:10.1177/1748302620931042