Image classification method and system based on small sample learning, storage medium and terminal

A small sample, sample technology, applied in storage media and terminals, image classification method based on small sample learning, system field, can solve the problem of the decline of the generalization ability of the classification model and the failure to make full use of the sample features.

Pending Publication Date: 2021-03-02
TERMINUSBEIJING TECH CO LTD
View PDF5 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The problems of metric-based learning methods are as follows: These methods embed the samples of the support set and query set into the fea...

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 and system based on small sample learning, storage medium and terminal
  • Image classification method and system based on small sample learning, storage medium and terminal
  • Image classification method and system based on small sample learning, storage medium and terminal

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0055] The following description and drawings illustrate specific embodiments of the invention sufficiently to enable those skilled in the art to practice them.

[0056] It should be clear that the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0057] When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of systems and methods consistent with aspects of the invention as recited in the appended claims.

[0058] In the description of...

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 and system based on small sample learning, a storage medium and a terminal. The method comprises the steps of determining a to-be-classified image data set; extracting a plurality of types of image data samples in the image data set, and establishing an image support set based on the plurality of types of image data samples; extracting a plurality of types of second image data samples from the remaining image data samples after extraction in the image data set to construct a query set; and performing generalization processing on a pre-trained small sample classification network according to the image support set, inputting each image data sample in the image query set into the generalized small sample classification network, and outputting the category of each image data sample. Therefore, by adopting the embodiment of the invention, the feature fusion processing is carried out by extracting the sample features of the support set and the query set, so that the two sets mutually enhance the related features between the samples to highlight the features interested between the samples, and the image classification precision is improved.

Description

technical field [0001] The invention relates to the field of computer deep learning technology, in particular to an image classification method, system, storage medium and terminal based on small sample learning. Background technique [0002] With the development of deep learning, people have proposed an image classification method based on deep neural network, which needs to be trained through a large number of samples, so that the deep neural network has better performance. However, in some practical applications, such as object tracking or object detection, we may only have limited samples, making it difficult to build a large set of valuable, labeled samples. Therefore, small sample learning is an application of meta-learning in the field of supervised learning. By constructing different meta-tasks to solve the m-way k-shot problem, it is suitable for improving the generalization ability of the classification model in the case of a small number of samples. [0003] Exis...

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): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/241G06F18/253G06F18/214
Inventor 赵磊方红波廖旻可
Owner TERMINUSBEIJING TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap