scholarly journals Image Classification via Sparse Representation and Subspace Alignment

2020 ◽  
Vol 17 (2) ◽  
pp. 58-47
Author(s):  
Farimah Sherafati ◽  
Jafar Tahmoresnezhad ◽  
◽  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Subspace Alignment

Weighted Discriminative Sparse Representation for Image Classification

2021 ◽  
Author(s):  
Zhen Liu ◽  
Xiao-Jun Wu ◽  
Zhenqiu Shu ◽  
Hefeng Yin ◽  
Zhe Chen
Keyword(s):  
Sparse Representation ◽  
Image Classification

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

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