• Title, Summary, Keyword: CNN algorithm

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Method that determining the Hyperparameter of CNN using HS algorithm (HS 알고리즘을 이용한 CNN의 Hyperparameter 결정 기법)

  • Lee, Woo-Young;Ko, Kwang-Eun;Geem, Zong-Woo;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.27 no.1
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    • pp.22-28
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    • 2017
  • The Convolutional Neural Network(CNN) can be divided into two stages: feature extraction and classification. The hyperparameters such as kernel size, number of channels, and stride in the feature extraction step affect the overall performance of CNN as well as determining the structure of CNN. In this paper, we propose a method to optimize the hyperparameter in CNN feature extraction stage using Parameter-Setting-Free Harmony Search (PSF-HS) algorithm. After setting the overall structure of CNN, hyperparameter was set as a variable and the hyperparameter was optimized by applying PSF-HS algorithm. The simulation was conducted using MATLAB, and CNN learned and tested using mnist data. We update the parameters for a total of 500 times, and it is confirmed that the structure with the highest accuracy among the CNN structures obtained by the proposed method classifies the mnist data with an accuracy of 99.28%.

Image Label Prediction Algorithm based on Convolution Neural Network with Collaborative Layer (협업 계층을 적용한 합성곱 신경망 기반의 이미지 라벨 예측 알고리즘)

  • Lee, Hyun-ho;Lee, Won-jin
    • Journal of Korea Multimedia Society
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    • v.23 no.6
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    • pp.756-764
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    • 2020
  • A typical algorithm used for image analysis is the Convolutional Neural Network(CNN). R-CNN, Fast R-CNN, Faster R-CNN, etc. have been studied to improve the performance of the CNN, but they essentially require large amounts of data and high algorithmic complexity., making them inappropriate for small and medium-sized services. Therefore, in this paper, the image label prediction algorithm based on CNN with collaborative layer with low complexity, high accuracy, and small amount of data was proposed. The proposed algorithm was designed to replace the part of the neural network that is performed to predict the final label in the existing deep learning algorithm by implementing collaborative filtering as a layer. It is expected that the proposed algorithm can contribute greatly to small and medium-sized content services that is unsuitable to apply the existing deep learning algorithm with high complexity and high server cost.

Object Detection based on Mask R-CNN from Infrared Camera (적외선 카메라 영상에서의 마스크 R-CNN기반 발열객체검출)

  • Song, Hyun Chul;Knag, Min-Sik;Kimg, Tae-Eun
    • Journal of Digital Contents Society
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    • v.19 no.6
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    • pp.1213-1218
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    • 2018
  • Recently introduced Mask R - CNN presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation mask of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask R - CNN is an algorithm that extends Faster R - CNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. The mask R - CNN is added to the high - speed R - CNN which training is easy and fast to execute. Also, it is easy to generalize the mask R - CNN to other tasks. In this research, we propose an infrared image detection algorithm based on R - CNN and detect heating elements which can not be distinguished by RGB images. As a result of the experiment, a heat-generating object which can not be discriminated from Mask R-CNN was detected normally.

A Study of Facial Organs Classification System Based on Fusion of CNN Features and Haar-CNN Features

  • Hao, Biao;Lim, Hye-Youn;Kang, Dae-Seong
    • The Journal of Korean Institute of Information Technology
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    • v.16 no.11
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    • pp.105-113
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    • 2018
  • In this paper, we proposed a method for effective classification of eye, nose, and mouth of human face. Most recent image classification uses Convolutional Neural Network(CNN). However, the features extracted by CNN are not sufficient and the classification effect is not too high. We proposed a new algorithm to improve the classification effect. The proposed method can be roughly divided into three parts. First, the Haar feature extraction algorithm is used to construct the eye, nose, and mouth dataset of face. The second, the model extracts CNN features of image using AlexNet. Finally, Haar-CNN features are extracted by performing convolution after Haar feature extraction. After that, CNN features and Haar-CNN features are fused and classify images using softmax. Recognition rate using mixed features could be increased about 4% than CNN feature. Experiments have demonstrated the performance of the proposed algorithm.

Classification and Restoration of Compositely Degraded Images using Deep Learning (딥러닝 기반의 복합 열화 영상 분류 및 복원 기법)

  • Yun, Jung Un;Nagahara, Hajime;Park, In Kyu
    • Journal of Broadcast Engineering
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    • v.24 no.3
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    • pp.430-439
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    • 2019
  • The CNN (convolutional neural network) based single degradation restoration method shows outstanding performance yet is tailored on solving a specific degradation type. In this paper, we present an algorithm of multi-degradation classification and restoration. We utilize the CNN based algorithm for solving image degradation classification problem using pre-trained Inception-v3 network. In addition, we use the existing CNN based algorithms for solving particular image degradation problems. We identity the restoration order of multi-degraded images empirically and compare with the non-reference image quality assessment score based on CNN. We use the restoration order to implement the algorithm. The experimental results show that the proposed algorithm can solve multi-degradation problem.

Centroid Neural Network with Bhattacharyya Kernel (Bhattacharyya 커널을 적용한 Centroid Neural Network)

  • Lee, Song-Jae;Park, Dong-Chul
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.9C
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    • pp.861-866
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    • 2007
  • A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

Analysis of Evolutionary Optimization Methods for CNN Structures (CNN 구조의 진화 최적화 방식 분석)

  • Seo, Kisung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.6
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    • pp.767-772
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    • 2018
  • Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

Binary CNN Operation Algorithm using Bit-plane Image (비트평면 영상을 이용한 이진 CNN 연산 알고리즘)

  • Choi, Jong-Ho
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.6
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    • pp.567-572
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    • 2019
  • In this paper, we propose an algorithm to perform convolution, pooling, and ReLU operations in CNN using binary image and binary kernel. It decomposes 256 gray-scale images into 8 bit planes and uses a binary kernel consisting of -1 and 1. The convolution operation of binary image and binary kernel is performed by addition and subtraction. Logically, it is a binary operation algorithm using the XNOR and comparator. ReLU and pooling operations are performed by using XNOR and OR logic operations, respectively. Through the experiments to verify the usefulness of the proposed algorithm, We confirm that the CNN operation can be performed by converting it to binary logic operation. It is an algorithm that can implement deep running even in a system with weak computing power. It can be applied to a variety of embedded systems such as smart phones, intelligent CCTV, IoT system, and autonomous car.

Analog Parallel Processing Algorithm of CNN-UM for Interframe Change Detection (프레임간의 영상 변화 검출을 위한 CNN-UM의 아날로그 병렬연산처리 알고리즘)

  • 김형석;김선철;손홍락;박영수;한승조
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.40 no.1
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    • pp.1-9
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    • 2003
  • The CNN-UM algorithm which performs the analog parallel subtraction of images has been developed and its application study to the moving target detection has been done. The CNN-UM is the state of the art computation architecture with high computational potential of analog parallel processing. It is one of the strong candidates for the next generation of computing system which fulfills requirement of the real-time image processing. One weakness of the CNN-UM is that its analog parallel processing function is not fully utilized for the inter frame processing. If two subsequent image frames are superimposed with opposite signs on identical capacitors for short time period, the analog subtraction between them is achieved. The Principle of such temporal inter-frame processing algorithm has been described and its mathematical analysis has been done. Practical usefulness of the proposed algorithm has also been verified through the application for moving target detection.

HVS-Aware Single-Shot HDR Imaging Using Deep Convolutional Neural Network (시각 인지 특성과 딥 컨볼루션 뉴럴 네트워크를 이용한 단일 영상 기반 HDR 영상 취득)

  • Vien, An Gia;Lee, Chul
    • Journal of Broadcast Engineering
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    • v.23 no.3
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    • pp.369-382
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    • 2018
  • We propose a single-shot high dynamic range (HDR) imaging algorithm using a deep convolutional neural network (CNN) for row-wise varying exposures in a single image. The proposed algorithm restores missing information resulting from under- and/or over-exposed pixels in an input image and reconstructs the raw radiance map. The main contribution of this work is the development of a loss function for the CNN employing the human visual system (HVS) properties. Then, the HDR image is obtained by applying a demosaicing algorithm. Experimental results demonstrate that the proposed algorithm provides higher-quality HDR images than conventional algorithms.