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Construction Method of Convolutional Neural Network Structure for Image Classification

A convolutional neural network and convolutional neural technology, applied in the field of convolutional neural network structure, can solve the problems of increasing the number of network parameters and increasing the complexity of network calculations, achieving reduced complexity, improved classification performance, and simple construction methods Effect

Active Publication Date: 2018-10-23
SHAANXI NORMAL UNIV
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AI Technical Summary

Problems solved by technology

[0004] The main technical problem in the above-mentioned image classification method based on neural networks is that in the process of network structure design, the number and size of filters, pooling methods, and activation functions are often determined by experience, and inappropriate network structures will greatly affect Increase the number of network parameters, leading to an increase in the complexity of network calculations

Method used

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  • Construction Method of Convolutional Neural Network Structure for Image Classification
  • Construction Method of Convolutional Neural Network Structure for Image Classification
  • Construction Method of Convolutional Neural Network Structure for Image Classification

Examples

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Embodiment 1

[0054] The image of this embodiment comes from the SVHN (The Street View House Numbers Dataset) dataset that Google extracts from the street view house number image in the real world. The 73257 images in this dataset are used in this embodiment as In the training set, 26032 images are used as the test set, and the training set and the test set do not overlap.

[0055] exist figure 1 Among them, the construction method of the image classification convolutional neural network structure of the present embodiment is composed of the steps of constructing the convolutional neural network structure, convolutional neural network training and testing, and the steps of constructing the convolutional neural network structure are as follows:

[0056] (1) Obtain training sample images and test sample images and preprocess them

[0057] (a) Select 73257 training sample images and 26032 testing sample images from the image dataset.

[0058] (b) Preprocessing 73257 training sample images

...

Embodiment 2

[0094] The images in this example come from the MNIST dataset consisting of handwritten digits. In this embodiment, 60,000 digital images in the data set are used as a training set, and 10,000 digital images are used as a test set, and the training set and the test set do not overlap.

[0095] The construction method of the image classification convolutional neural network structure of the present embodiment is made up of construction convolutional neural network structure, convolutional neural network training and testing steps, and the steps of constructing convolutional neural network structure are as follows:

[0096] (1) Obtain training sample images and test sample images

[0097] (a) Select 60,000 training sample images and 10,000 testing sample images from the image dataset.

[0098] (b) Preprocessing 60,000 training sample images

[0099] The steps for pretreatment are the same as in Example 1.

[0100] (c) Preprocess 10,000 test sample images

[0101] The preproc...

Embodiment 3

[0120] The images in this embodiment come from an ASL (American Sign Language, ASL) data set composed of gesture images. In this embodiment, 50,400 gesture images in the data set are used as a training set, and 6,000 gesture images are used as a test set, and the training set and the test set do not overlap.

[0121] The construction method of the image classification convolutional neural network structure of the present embodiment is made up of construction convolutional neural network structure, convolutional neural network training and testing steps, and the steps of constructing convolutional neural network structure are as follows:

[0122] (1) Obtain training sample images and test sample images and preprocess them

[0123] (a) Select 50400 training sample images and 6000 testing sample images from the image dataset.

[0124] (b) Preprocessing 50400 training sample images

[0125] Preprocessing the training sample images is the same as that in Embodiment 1.

[0126] (...

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Abstract

A method for constructing a convolutional neural network structure for image classification consists of steps of constructing a convolutional neural network structure, training and testing the convolutional neural network. The steps to build a convolutional neural network structure are: obtain training sample images and test sample images and preprocess them, set the initial structure of the convolutional neural network, introduce activation functions, determine the pooling method, determine the filter, and determine the convolution Layer number, obtain image classification convolutional neural network structure; The training and testing steps of convolutional neural network are: obtain the filter of the convolutional layer that contains 20 feature maps with K-Means clustering method, through forward propagation and Backpropagation updates the network weight matrix to the maximum number of training times, obtains a well-trained convolutional neural network, tests the test sample images, and obtains a verified image classification convolutional neural network structure, which has the advantages of simple and effective construction methods, It is suitable for image classification such as house numbers, handwritten numbers, postal codes, etc.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and in particular relates to a convolutional neural network structure suitable for image classification. Background technique [0002] Deep learning is a new field in machine learning research. Its motivation is to establish and simulate the neural network of human brain for analysis and learning. It interprets data, such as images, sounds, and texts, by imitating the mechanism of the human brain. Its core is to learn more useful features by building a machine learning model with multiple hidden layers and a large amount of training data, thereby ultimately improving classification or forecast accuracy. [0003] Existing representative networks such as LeNet-5, AlexNet, GoogLeNet, and ResNet are all based on convolutional neural network structures. Among them, LeNet-5 consists of 7 layers, its 1st, 3rd, and 5th layers are convolutional layers, and 2nd and 4th la...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06N3/084G06F18/23213
Inventor 马苗刘琳武杰陈昱莅裴炤
Owner SHAANXI NORMAL UNIV