An Analysis on the Two-phase Test Sample Sparse Representation Method and an Improved Method

2011 ◽  
Author(s):  
Yuwu Lu
Keyword(s):  
Sparse Representation ◽  
Test Sample ◽  
Improved Method ◽  
Two Phase ◽  
Representation Method

A Kernel Two-Phase Test Sample Sparse Representation for Face Recognition

2015 ◽  
Vol 30 (01) ◽  
pp. 1656001
Author(s):  
Zhonghua Liu ◽  
Jiexin Pu ◽  
Yong Qiu ◽  
Moli Zhang ◽  
Xiaoli Zhang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Recognition Performance ◽  
Mapping Function ◽  
Feature Space ◽  
Test Sample ◽  
Training Data ◽  
Two Phase ◽  
Kernel Parameter ◽  
Representation Method

Sparse representation is a new hot technique in recent years. The two-phase test sample sparse representation method (TPTSSR) achieved an excellent performance in face recognition. In this paper, a kernel two-phase test sample sparse representation method (KTPTSSR) is proposed. Firstly, the input data are mapped into an implicit high-dimensional feature space by a nonlinear mapping function. Secondly, the data are analyzed by means of the TPTSSR method in the feature space. If an appropriate kernel function and the corresponding kernel parameter are selected, a test sample can be accurately represented as the linear combination of the training data with the same label information of the test sample. Therefore, the proposed method could have better recognition performance than TPTSSR. Experiments on the face databases demonstrate the effectiveness of our methods.

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.

scholarly journals Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jiajia Liu ◽  
Bailin Li ◽  
Ying Xiong ◽  
Biao He ◽  
Li Li
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Identification Problem ◽  
Test Sample ◽  
Computationally Efficient ◽  
Railway Traffic ◽  
Training Samples ◽  
Inspection Systems ◽  
Representation Method ◽  
Defect Recognition

The detection of fastener defects is an important task for ensuring the safety of railway traffic. The earlier automatic inspection systems based on computer vision can detect effectively the completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In this paper, we propose a method for detecting both partly worn and completely missing fasteners, the proposed algorithm exploits the first and second symmetry sample of original testing fastener image and integrates them for improved representation-based fastener recognition. This scheme is simple and computationally efficient. The underlying rationales of the scheme are as follows: First, the new virtual symmetrical images really reflect some possible appearance of the fastener; then the integration of two judgments of the symmetrical sample for fastener recognition can somewhat overcome the misclassification problem. Second, the improved sparse representation method discarding the training samples that are “far” from the test sample and uses a small number of samples that are “near” to the test sample to represent the test sample, so as to perform classification and it is able to reduce the side-effect of the error identification problem of the original fastener image. The experimental results show that the proposed method outperforms state-of-the-art fastener recognition methods.

Novel classification rule of two-phase test sample sparse representation

Optik ◽  
2014 ◽  
Vol 125 (19) ◽  
pp. 5825-5832 ◽  
Author(s):  
Qingxiang Feng ◽  
Xingjie Zhu ◽  
Jeng-Shyang Pan
Keyword(s):  
Sparse Representation ◽  
Test Sample ◽  
Classification Rule ◽  
Two Phase
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