- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Nearest Centroid Neighbor Based Sparse Representation Classification for Finger vein Recognition
摘要： In this paper, an efficient finger vein recognition algorithm based on the combination of the nearest centroid neighbor and sparse representation classification techniques (kNCN-SRC) is presented. The previously proposed recognition algorithms are mainly based on distance computation. In the proposed method, the distance, as well as the spatial distribution, are considered to achieve a better recognition rate. The proposed method consists of two stages: first, the k nearest neighbors of the test sample are selected based on the nearest centroid neighbor and then in the second stage based on the selected number of closest nearest centroid neighbors (k) the test sample is classified by sparse representation. Findings from the proposed method kNCN-SRC demonstrated an increased recognition rate. This improvement can be attributed to the selection of the train samples, where the train samples are selected by considering the spatial and distance distribution. In addition, the complexity of SRC is reduced by reducing the number of train samples for classification of the test sample by sparse representation and the processing speed of the proposed algorithm is significantly improved in comparison to the conventional SRC which is due to the reduced number of training samples. It can be concluded that the kNCN-SRC classification method is efficient for finger vein recognition. An increase in the recognition rate of 3.35%, 9.07%, 20.23%, and 0.81% is obtained for the proposed kNCN-SRC method in comparison with the conventional SRC for the four tested public finger vein databases.
关键词： Finger vein recognition,Distance criterion,k-Nearest Centroid Neighbor,Spatial distribution,Sparse Representation Classification
更新于2025-09-23 15:22:29
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[IEEE 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hangzhou (2018.10.18-2018.10.20)] 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) - Hyperspectral Image Classification VIA a Joint Sparsity and Spatial Correlation Model
摘要： In this paper, a novel constrained Sparse Representation (SR) algorithm based on the joint sparsity and spatial correlation for hyperspectral image (HSI) classification is proposed. The coefficients in the sparse vector associated with the training samples in the structured dictionary exhibit the group sparsity continuity. However, this joint sparsity of the coefficient vector is not considered in the classical SR classifiers. In addition, spatial correlation has positive effect on HSI classification processing. Thus in the proposed SR model, we consider a joint sparsity regularization term to promote the joint sparsity of the sparse vectors and use space regularization to restrict spatial correlation of the output. The formulated problem is solved via the alternating direction method of multipliers (ADMM). Simulation results show that the proposed algorithm has the improved performance.
关键词： sparse representation,classification,Hyperspectral imagery,joint sparsity,ADMM
更新于2025-09-23 15:22:29
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Hyperspectral image classification via compact-dictionary-based sparse representation
摘要： In this paper, a compact-dictionary-based sparse representation (CDSR) method is proposed for hyperspectral image (HSI) classification. The proposed dictionary in CDSR is dynamically generated according to the spatial and spectral context of each pixel. It can effectively shrink the decision range for classification, and reduce the computational burden since the compact dictionary is composed of the classes correlated with the target pixel in terms of spatial location and spectral information. In order to obtain better spatial context information, a spatial location expanding strategy is designed for spreading local explicit label information to a wider region. Experimental results demonstrate the effectiveness and superiority of the proposed method when compared with some widely used HSI classification approaches.
关键词： Compact dictionary,Hyperspectral image,Sparse representation,Classification
更新于2025-09-23 15:21:21