scholarly journals Research on image classification model based on deep convolution neural network

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Mingyuan Xin ◽  
Yong Wang
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Model Based ◽  
Deep Convolution Neural Network

scholarly journals Deep Convolution Neural Network for Big Data Medical Image Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 105659-105670 ◽  
Author(s):  
Rehan Ashraf ◽  
Muhammad Asif Habib ◽  
Muhammad Akram ◽  
Muhammad Ahsan Latif ◽  
Muhammad Sheraz Arshad Malik ◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Image Classification ◽  
Medical Image ◽  
Convolution Neural Network ◽  
Deep Convolution Neural Network ◽  
Medical Image Classification

scholarly journals Application Research of Deep Convolutional Neural Network in Computer Vision

2020 ◽  
Vol 2 (2) ◽  
pp. 23
Author(s):  
Lei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Face Recognition ◽  
Human Brain ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Convolution Neural Network ◽  
Data Set ◽  
Deep Convolution Neural Network

<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of "abstract concepts". Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.</p>

scholarly journals Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

2017 ◽  
Vol 7 (5) ◽  
pp. 447 ◽  
Author(s):  
Fei Gao ◽  
Teng Huang ◽  
Jun Wang ◽  
Jinping Sun ◽  
Amir Hussain ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Convolution Neural Network ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Deep Convolution Neural Network

scholarly journals Bare Skin Image Classification using Convolution Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 138-145
Author(s):  
Jaya Gupta ◽  
◽  
Sunil Pathak ◽  
Gireesh Kumar
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Satellite Image ◽  
Activation Function ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Significant Research ◽  
Research Problems ◽  
Facial Expression Classification ◽  
Expression Classification

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, and plant classification based on images. Here in the paper, the image classification model is applied for identifying the display of daunting pictures on the internet. The proposed model uses Convolution neural network to identify these images and filter them through different blocks of the network, so that it can be classified accurately. The model will work as an extension to the web browser and will work on all websites when activated. The extension will be blurring the images and deactivating the links on web pages. This means that it will scan the entire web page and find all the daunting images present on that page. Then we will blur those images before they are loaded and the children could see them. Keywords— Activation Function, CNN, Images Classification , Optimizers, VGG-19

scholarly journals Image classification model based on spark and CNN

2018 ◽  
Vol 189 ◽  
pp. 03012
Author(s):  
Jiangfeng Xu ◽  
Shenyue Ma
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Great Influence ◽  
Image Data ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Feature Maps ◽  
Single Node ◽  
Network Nodes ◽  
And Performance

Convolution neural network is a commonly used image classification model, but when the network nodes of the training process are too many, it will have a great influence on the training complexity. At the same time, when the size of the image data is large, many problems will appear on the single node, such as convergence slowly, frequently disk reading and writing. In order to overcome the above problems, this paper proposes a distributed convolution neural network based on Spark (Distribution Convolution neural network, Dis-CNN) model. The model first improves the initialization mode of convolution kernel parameters, then eliminates the redundancy of feature maps, and finally optimizes the distributed gradient descent by reducing the synchronous traffic between master and slave, so as to improve the convergence speed and performance. The experimental results show that the model not only improves the accuracy and recall of image classification, but also performs excellent in parallelism.

Sign in / Sign up

Export Citation Format

Share Document