Correntropy Extreme Learning Machine Based on Spatial Pyramid Matching and Local Receptive Field

Bin LIU; Jing LIU; Chao WU; Youheng YANG

doi:10.11999/JEIT200562

Volume 43 Issue 8

Aug. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(8): 2343-2351

Bin LIU, Jing LIU, Chao WU, Youheng YANG. Correntropy Extreme Learning Machine Based on Spatial Pyramid Matching and Local Receptive Field[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2343-2351. doi: 10.11999/JEIT200562

Citation:

Bin LIU, Jing LIU, Chao WU, Youheng YANG. Correntropy Extreme Learning Machine Based on Spatial Pyramid Matching and Local Receptive Field[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2343-2351. doi: 10.11999/JEIT200562

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Correntropy Extreme Learning Machine Based on Spatial Pyramid Matching and Local Receptive Field

doi: 10.11999/JEIT200562

Bin LIU^{1
,
,},
Jing LIU¹,
Chao WU²,
Youheng YANG¹

1.
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Funds: The Natural Science Foundation of Hebei Province (F2019203320, E2018203398)

Received Date: 2020-06-29
Rev Recd Date: 2020-12-05

Available Online: 2020-12-16

Publish Date: 2021-08-10

Abstract

Abstract

Considering the problems of inefficient use of spatial information between features and inadequate fusion of different features, a Correntropy Extreme Learning Machine based on Spatial pyramid matching and local Receptive field(SR-CELM) is proposed. In feature extraction part, multi-scale local receptive fields are used to convolve the generated multi-level dictionary feature distribution map, and local position features and global contour features are introduced. In feature classification part, a new network is proposed to fuse the features of each part. Based on the traditional extreme learning machine training method, a discriminative constraint is constructed by using the relevant entropy criterion, and the weight update formula is used to solve the output weight of the new network. In order to verify the effectiveness of the SR-CELM, experiments are performed on the databases Caltech 101, MSRC and 15 Scene. The experiments show that SR-CELM can make full use of the identifiable information in the features and improve the classification accuracy.
- Image classification,
- Bag of words model,
- Local receptive field,
- Extreme learning machine,
- Correntropy

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References(22)

References

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