Image Recognition based on Deep Learning Using Multi-Dimensional Neural Network and Decision Making Neural Network

Sang-Heon Lee; Dong-Yeon Kim; Youngwan Cho

doi:10.5391/JKIIS.2018.28.1.34

Image Recognition based on Deep Learning Using Multi-Dimensional Neural Network and Decision Making Neural Network

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다차원 신경망 및 의사결정 신경망의 딥러닝 학습을 통한 영상인식

Type: Academic journal

Author: Sang-Heon Lee (서경대학교) Dong-Yeon Kim (한경대학교) Youngwan Cho (서경대학교)

Journal: Korean Institute of Intelligent Systems Journal of Korean Institute of Intelligent Systems Vol.28 No.1 KCI Accredited Journals

Published: 2018.2

Pages: 34 - 40 (7page)

DOI: 10.5391/JKIIS.2018.28.1.34

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Image Recognition based on Deep Learning Using Multi-Dimensional Neural Network and Decision Making Neural Network

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2018

Image Recognition based on Deep Learning Using Multi-Dimensional Neural Network and Decision Making Neural Network

Sang-Heon Lee , Dong-Yeon Kim , Youngwan Cho Journal of Korean Institute of Intelligent Systems 2018.02 Academic journal

이상헌 전자컴퓨터공 2018.01 Thesis

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Recently, deep learning has been widely recognized as a value in the field of computer vision, and it is widely used for solving problems in actual vision field as a method of image recognition among computer vision fields. However, the physical and temporal space required to perform the deep learning algorithm remains a disadvantage. Therefore, in this paper, we design a multi-dimensional neural network to secure it, and propose a new network structure that makes decision about the result of multidimensional neural network using decision-making neural network learned from reconstruction data and explain the learning method using it. We construct a multi-dimensional neural network using complementary neural network that learns main neural network to show proper performance and learn about images which are not recognized in main neural network and derive final result through decision neural network through output of multi - dimensional neural network. At this time, the reconstructed data is used as the input data of the decision-making neural network, and the data reconstruction uses the additional information that can be obtained by analyzing the output results of the multi-dimensioanl neural network and the training data.

#딥 러닝 #다차원 신경망 #의사결정 신경망 #데이터 재구성 #영상 인식 #Deep Learning #Multi-Dimensional Neural Network #Decision-Mkaing Neural Network #Data Reconstruction #Image Recognition

요약
Abstract
1. 서론
2. 제안한 네트워크의 설계와 데이터 재구성
3. 구현 및 실험결과
4. 결론 및 향후 연구
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BENGIO, Yoshua, et al. Learning deep architectures for AI. Foundations and trends®in Machine Learning, 2009, 2.1: 1-127. J. Lee, J. Lee, C. Hyun, “Coin Recognition and Classification Using Digital Image Processing” Journal of The Korean Institute of Intelligent Systems 2012, vol.22, No.1, p.7-11. S. Seong, C. Ma, S. Yu, “Design of Optimized pRBFNNsbased Face Recognition Algorithm Using Two-dimensional Image and ASM Algorithm” Journal of The Korean Institute of Intelligent Systems 2011, vol.21, No.6, p.749-754. K. Ko, S. Park, J. Kim, G. Sim, “HMM-based intent Recognition System using 3D Image Reconstruction Data” Journal of The Korean Institute of Intelligent Systems 2012, vol.22, No.2, p.135-140. H. Choi, K. Seo, “CNN Based Detection of Surface Defects for Electronic Parts” Journal of The Korean Institute of Intelligent Systems 2017, vol.27, No.3, p.195-200.

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UCI(KEPA) : I410-ECN-0101-2018-003-001767137

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