Schistosoma Cercariae Image Recognition via Sparse Representation

2011 ◽  
Author(s):  
Shenhai Yan ◽  
Yang Liu ◽  
Xiantong Huang
Keyword(s):  
Sparse Representation ◽  
Image Recognition

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 Occluded Face Recognition Based on Double Layers Module Sparsity Difference

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shuhuan Zhao ◽  
Zheng-ping Hu
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Sparse Representation ◽  
Image Recognition ◽  
Double Layers ◽  
Four Blocks

Image recognition with occlusion is one of the popular problems in pattern recognition. This paper partitions the images into some modules in two layers and the sparsity difference is used to evaluate the occluded modules. The final identification is processed on the unoccluded modules by sparse representation. Firstly, we partition the images into four blocks and sparse representation is performed on each block, so the sparsity of each block can be obtained; secondly, each block is partitioned again into two modules. Sparsity of each small module is calculated as the first step. Finally, the sparsity difference of small module with the corresponding block is used to detect the occluded modules; in this paper, the small modules with negative sparsity differences are considered as occluded modules. The identification is performed on the selected unoccluded modules by sparse representation. Experiments on the AR and Yale B database verify the robustness and effectiveness of the proposed method.

Learning sparse representation for leaf image recognition

2014 ◽  
Author(s):  
Jou-Ken Hsiao ◽  
Li-Wei Kang ◽  
Ching-Long Chang ◽  
Chao-Yung Hsu ◽  
Chia-Yen Chen
Keyword(s):  
Sparse Representation ◽  
Image Recognition ◽  
Leaf Image
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