Alignment-free sparse representation based classification method via fast location

2014 ◽  
Author(s):  
Jun He ◽  
Cheng Li ◽  
Bo Sun ◽  
Xuewen Wu ◽  
Fengxiang Ge
Keyword(s):  
Sparse Representation ◽  
Classification Method ◽  
Alignment Free

Image Classification Base on Sparse Representation with Basis Design

2013 ◽  
Vol 709 ◽  
pp. 515-518
Author(s):  
Hao Feng Huang ◽  
Shao Ying Chen
Keyword(s):  
Representation Theory ◽  
Sparse Representation ◽  
Image Classification ◽  
Design Method ◽  
Classification Model ◽  
Classification Method ◽  
Sparse Model

An image classification method based on sparse representation with basis design is proposed. We construct a classification model under the sparse representation theory. The sparse model can lead to be better performance under a suitable dictionary, so the basis design method can follow the same process as discussed in[6].The experiments show that the proposed method improves the result.

scholarly journals Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5559
Author(s):  
Na Li ◽  
Ruihao Wang ◽  
Huijie Zhao ◽  
Mingcong Wang ◽  
Kewang Deng ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Matching Pursuit ◽  
Small Sample Size ◽  
Small Sample ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Diverse Density

To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91.

scholarly journals An efficient classification method based on principal component and sparse representation

SpringerPlus ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Lin Zhai ◽  
Shujun Fu ◽  
Caiming Zhang ◽  
Yunxian Liu ◽  
Lu Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Principal Component ◽  
Classification Method
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