特征空间中基于半遗传稀疏表示的图像识别

doi:10.11959/j.issn.2096-6652.202137

Abstract

Abstract:

The typical sparse representation for classification (SRC) is usually based on L₁minimization problem.Conceptually, SRC is essentially an L₀norm minimization problem solved in the original input space, which cannot capture well the nonlinear information within the data.In order to address this problem, a nonlinear mapping to map the original input data into a new high dimensional feature space was applied, and a new representation approach based on L₀norm was proposed.The representing dictionary used to represent the test sample contains two parts in the proposed approach.The first part is fixed to the neighbors of the test sample.The training samples of the second part is chosen by the variation of genetic algorithm (GA), i.e., the semi GA (SGA) algorithm, which exploits the representation error to determine the second part of the representing dictionary.In the approach, if the training samples combining the determined neighbors of the test sample yield the least representation error, these training samples are determined as the second part of the representing dictionary by SGA.Experiments on several popular face databases and one handwritten digit data set demonstrate that the proposed approach can achieve better classification performance.

Key words: sparse representation, image recognition, feature space, L₀ norm, genetic algorithm

CLC Number:

TP391.4

Linrui SHI, Yijing HUANG, Jinwu FU, et al. Sparse representation for image recognition based on semi-genetic algorithm in feature space[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 359-369.

Figures/Tables 10

References 52

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算法	M=5	M=10	M=15
CRC	62.09%±1.76	75.75%±1.71	82.65%±1.36
TPTSR	59.43%±2.02	75.05%±1.59	82.91%±0.81
SRC	61.78%±1.84	77.13%±1.77	84.84%±0.92
KSRC	66.63%±1.61	80.43%±1.47	86.33%±0.61
KCD	70.49%±1.98	82.54%±1.65	87.99%±1.04
KED	53.96%±1.94	63.95%±1.35	69.82%±1.50
KSVD	68.73%±1.95	78.67%±1.81	81.66%±1.34
LCLE	70.25%±1.93	81.26%±0.89	84.35%±1.07
ESRC	68.60%±2.08	82.45%±1.75	88.46%±0.93
KGSRSN	46.55%±0.54	66.06%±0.52	77.20%±0.35
DFEDL	62.85%±0.71	78.58%±0.68	86.35%±0.39
KGASR	71.55%±1.65	85.25%±0.99	91.05%±0.22

算法	M=4	M=5	M=6	M=7
CRC	60.05%±1.22	64.48%±1.68	68.85%±1.3	71.59%±1.96
TPTSR	60.16%±1.18	65.22%±1.76	70.34%±1.49	74.47%±1.26
SRC	61.59%±1.64	67.01%±2.01	71.92%±1.56	75.59%±1.45
KSRC	62.25%±0.93	66.97%±1.8	70.81%±1.3	74.18%±1.37
KCD	63.16%±1.26	67.64%±1.95	72.09%±1.49	75.42%±1.18
KED	62.4%±3.4	67.35%±2.75	71.28%±2.41	74.9%±2.49
KSVD	63.3%±1.39	67.65%±1.87	69.05%±1.23	71.55%±1.56
LCLE	63.72%±1.17	67.41%±1.92	70.63%±0.98	73.21%±1.14
ESRC	62.84%±1.47	67.50%±1.91	72.72%±1.24	76%±1.3
KGSRSN	43.8%±0.35	49.96%±0.47	54.77%±0.57	60.51%±0.46
DFEDL	31.52%±0.31	61.59%±0.54	66.29%±0.46	70.34%±0.47
KGASR	66.25%±1.44	71.31%±1.69	75.7%±1.14	76.86%±1.03

算法	M=3	M=4
CRC	78.86%±1.74	84.07%±0.97
TPTSR	78.75%±1.38	84.06%±0.88
SRC	78.39%±1.31	84.19%±0.7
KSRC	79.4%±1.33	84.69%±0.83
KCD	80.97%±1.52	86.14%±0.75
KED	82.54%±1.19	87.31%±0.91
KSVD	75.24%±1.77	81.94%±0.91
LCLE	78.9%±1.54	82.86%±0.96
ESRC	81.25%±1.22	86.46%±1.23
KGSRSN	64.5%±0.47	72.49%±0.35
DFEDL	78.59%±0.59	84.37%±0.27
KGASR	82.99%±1.45	87.47%±0.82

Sparse representation for image recognition based on semi-genetic algorithm in feature space

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