The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam kertas kerja ini, kami mencadangkan model pengecaman visual yang mendalam berdasarkan Rangkaian KPCA hibrid(H-KPCANet), yang berdasarkan gabungan KPCANet satu peringkat dan KPCANet dua peringkat. Model yang dicadangkan terdiri daripada empat jenis komponen asas: lapisan input, KPCANet satu peringkat, KPCANet dua peringkat dan lapisan gabungan. Peranan KPCANet satu peringkat adalah untuk mengira penapis KPCA untuk lapisan konvolusi, dan KPCANet dua peringkat adalah untuk mempelajari penapis PCA pada peringkat pertama dan penapis KPCA pada peringkat kedua. Selepas pemetaan kuantisasi binari dan histogram mengikut blok, ciri-ciri daripada dua jenis KPCANets yang berbeza digabungkan dalam lapisan gabungan. Ciri akhir imej input boleh dicapai dengan gabungan bersiri berwajaran bagi kedua-dua jenis ciri. Prestasi algoritma cadangan kami diuji pada pengecaman digit dan klasifikasi objek, dan keputusan percubaan pada tanda aras pengecaman visual MNIST dan CIFAR-10 mengesahkan prestasi H-KPCANet yang dicadangkan.
Feng YANG
University of Electronic Science and Technology of China,Wenzhou Medical University
Zheng MA
University of Electronic Science and Technology of China
Mei XIE
University of Electronic Science and Technology of China
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Feng YANG, Zheng MA, Mei XIE, "Visual Recognition Method Based on Hybrid KPCA Network" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 9, pp. 2015-2018, September 2020, doi: 10.1587/transinf.2020EDL8041.
Abstract: In this paper, we propose a deep model of visual recognition based on hybrid KPCA Network(H-KPCANet), which is based on the combination of one-stage KPCANet and two-stage KPCANet. The proposed model consists of four types of basic components: the input layer, one-stage KPCANet, two-stage KPCANet and the fusion layer. The role of one-stage KPCANet is to calculate the KPCA filters for convolution layer, and two-stage KPCANet is to learn PCA filters in the first stage and KPCA filters in the second stage. After binary quantization mapping and block-wise histogram, the features from two different types of KPCANets are fused in the fusion layer. The final feature of the input image can be achieved by weighted serial combination of the two types of features. The performance of our proposed algorithm is tested on digit recognition and object classification, and the experimental results on visual recognition benchmarks of MNIST and CIFAR-10 validated the performance of the proposed H-KPCANet.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8041/_p
Salinan
@ARTICLE{e103-d_9_2015,
author={Feng YANG, Zheng MA, Mei XIE, },
journal={IEICE TRANSACTIONS on Information},
title={Visual Recognition Method Based on Hybrid KPCA Network},
year={2020},
volume={E103-D},
number={9},
pages={2015-2018},
abstract={In this paper, we propose a deep model of visual recognition based on hybrid KPCA Network(H-KPCANet), which is based on the combination of one-stage KPCANet and two-stage KPCANet. The proposed model consists of four types of basic components: the input layer, one-stage KPCANet, two-stage KPCANet and the fusion layer. The role of one-stage KPCANet is to calculate the KPCA filters for convolution layer, and two-stage KPCANet is to learn PCA filters in the first stage and KPCA filters in the second stage. After binary quantization mapping and block-wise histogram, the features from two different types of KPCANets are fused in the fusion layer. The final feature of the input image can be achieved by weighted serial combination of the two types of features. The performance of our proposed algorithm is tested on digit recognition and object classification, and the experimental results on visual recognition benchmarks of MNIST and CIFAR-10 validated the performance of the proposed H-KPCANet.},
keywords={},
doi={10.1587/transinf.2020EDL8041},
ISSN={1745-1361},
month={September},}
Salinan
TY - JOUR
TI - Visual Recognition Method Based on Hybrid KPCA Network
T2 - IEICE TRANSACTIONS on Information
SP - 2015
EP - 2018
AU - Feng YANG
AU - Zheng MA
AU - Mei XIE
PY - 2020
DO - 10.1587/transinf.2020EDL8041
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2020
AB - In this paper, we propose a deep model of visual recognition based on hybrid KPCA Network(H-KPCANet), which is based on the combination of one-stage KPCANet and two-stage KPCANet. The proposed model consists of four types of basic components: the input layer, one-stage KPCANet, two-stage KPCANet and the fusion layer. The role of one-stage KPCANet is to calculate the KPCA filters for convolution layer, and two-stage KPCANet is to learn PCA filters in the first stage and KPCA filters in the second stage. After binary quantization mapping and block-wise histogram, the features from two different types of KPCANets are fused in the fusion layer. The final feature of the input image can be achieved by weighted serial combination of the two types of features. The performance of our proposed algorithm is tested on digit recognition and object classification, and the experimental results on visual recognition benchmarks of MNIST and CIFAR-10 validated the performance of the proposed H-KPCANet.
ER -