Supercharge Your Innovation With Domain-Expert AI Agents!

Image classification method based on binary image classification network

A technology for binarizing images and classifying networks, applied in neural learning methods, biological neural network models, instruments, etc.

Active Publication Date: 2021-09-14
UNIV OF ELECTRONIC SCI & TECH OF CHINA
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of image classification, and proposes an image classification method based on binary image classification network

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image classification method based on binary image classification network
  • Image classification method based on binary image classification network
  • Image classification method based on binary image classification network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0057] Embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0058] Such as figure 1 As shown, the present invention provides a kind of image classification method based on binary image classification network, comprises the following steps:

[0059] S1: collect the original image, and initialize the original image;

[0060] S2: Build an image classification network based on the initialized original image;

[0061] S3: Image classification using the softmax classifier of the image classification network.

[0062] In the embodiment of the present invention, such as figure 2 As shown, step S1 includes the following sub-steps:

[0063] S11: Collect an original image with a size of 224*224*3, and add 0 elements with a width of 3 around the original image to obtain a first output image with a size of 230*230*3;

[0064] S12: Use a convolution kernel with a size of 7*7 and a step size of 1 to perform a convolution ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an image classification method based on a binary image classification network, and the method comprises the following steps: S1, collecting an original image, and initializing the original image; S2, building an image classification network according to the initialized original image; S3, carrying out image classification by using a softmax classifier of the image classification network. According to the image classification method, binarization processing is carried out on a convolution kernel of a convolution operation module with the largest operation amount in traditional image classification, linear approximation is carried out by using four binarized convolution kernels of the same specification, and the overhead of algorithm storage space is saved.

Description

technical field [0001] The invention belongs to the technical field of image classification, and in particular relates to an image classification method based on a binary image classification network. Background technique [0002] In recent years, Deep Neural Network (DNN) has brought revolutionary changes in the field of machine learning and pattern recognition. However, most of the existing DNN models are computationally expensive and memory intensive, which hinders their deployment in devices with low memory resources or in applications with strict latency requirements. [0003] Deep Neural Network (DNN) has brought revolutionary changes in the field of machine learning and pattern recognition. Take image classification as an example: LeNet, AlexNet, ResNet, VggNet and other classic network structures have been proposed one after another. These structures are mainly aimed at the server side. Both training and reasoning are performed in a hardware environment with suffic...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/62G06K9/38G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06F18/2415
Inventor 刘启和王钰涵周世杰张准董婉祾但毅严张豹
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com