Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Domain generalization image classification method based on low-rank constraint local regression

A low-rank constraint, local regression technique, applied in the field of computer vision

Pending Publication Date: 2022-05-13
NINGBO POLYTECHNIC
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, how to efficiently divide training samples into multiple clusters to discover feature latent domains is a challenging task

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
  • Domain generalization image classification method based on low-rank constraint local regression
  • Domain generalization image classification method based on low-rank constraint local regression
  • Domain generalization image classification method based on low-rank constraint local regression

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0023] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0024] The embodiment of the present invention relates to a domain generalization image classification method based on low-rank constrained local regression. How to ensure the robustness of visual classification when the target samples are not available in the training process is an important issue facing the current visual learning community. challenge. The domain generalization generated in this case has become a new researc...

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 relates to a domain generalization image classification method based on low-rank constraint local regression, and the method comprises the steps: (1), obtaining a source domain, and decomposing the source domain into a plurality of sub-domains; (2) training according to each sub-domain to obtain a classifier; step (3), constructing a low-rank constraint local regression framework according to the classifier obtained by training each sub-domain; and step (4), classifying the image based on the low-rank constraint local regression framework. The method can effectively classify the images, and can be applied to domain self-adaption.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a domain generalization image classification method based on low-rank constrained local regression. Background technique [0002] Humans are good at using past experience to solve problems quickly in new environments. Likewise, we expect machines to also build robust models under different conditions (domains) to solve many new tasks. For example, we want computer vision systems to be able to recognize objects immediately when the camera environment changes, without retraining. Methods to solve this problem can be classified into domain adaptation (DA) and domain generalization (DG). Domain Adaptation (DA) methods have been successfully applied to a wide range of vision applications by reducing the distribution gap in different but related domains and using source and target domain samples to train a target domain-specific model. As a related research problem, domain g...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/764G06V10/774G06K9/62G06F17/16
CPCG06F17/16G06F18/24147G06F18/214
Inventor 陶剑文但雨芳潘婕
Owner NINGBO POLYTECHNIC
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products