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

Image recognition method and device and terminal equipment

An image recognition and sample image technology, applied in the field of image processing, can solve the problem of low accuracy of image recognition methods, and achieve the effect of improving image recognition accuracy, improving effect, and good training.

Pending Publication Date: 2020-04-28
SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
View PDF2 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiment of the present application provides an image recognition method, device and terminal equipment to solve the problem of low accuracy of existing image recognition methods

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 recognition method and device and terminal equipment
  • Image recognition method and device and terminal equipment
  • Image recognition method and device and terminal equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] The following describes an image recognition method provided in the first embodiment of the present application, please refer to the attachment figure 1 , The image recognition method in the first embodiment of this application includes:

[0037] Step S101: Obtain a sample training set, the training sample set includes at least one set of sample image groups and sample labels corresponding to the sample image groups, and each set of sample image groups includes two sample images;

[0038] When using neural networks for image recognition, the selection of neural network types, model settings and model training are closely related to the accuracy of neural network recognition.

[0039] In the image recognition method of this embodiment, a twin neural network is selected as the neural network for image recognition. The twin neural network is a conjoined neural network. The twin neural network has two inputs, which can measure the similarity of the two inputs, and can be well reco...

Embodiment 2

[0086] The second embodiment of the application provides an image recognition device. For ease of description, only the parts related to the application are shown, such as figure 2 As shown, the image recognition device includes,

[0087] The training sample module 201 is configured to obtain a sample training set, the training sample set includes at least one set of sample image groups and sample labels corresponding to the sample image groups, and each sample image group includes two sample images;

[0088] The feature extraction module 202 is configured to perform a feature extraction operation. The feature extraction operation includes inputting the sample image group in the sample training set into the twin neural network to obtain the first feature vector and the first feature vector corresponding to the sample image group. Two feature vectors, where the first feature vector and the second feature vector are both normalized feature vectors;

[0089] The loss calculation module...

Embodiment 3

[0105] image 3 It is a schematic diagram of a terminal device provided in Embodiment 3 of the present application. Such as image 3 As shown, the terminal device 3 of this embodiment includes a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and running on the processor 30. When the processor 30 executes the computer program 32, the steps in the image recognition method embodiment described above are implemented, for example figure 1 Steps S101 to S105 are shown. Alternatively, when the processor 30 executes the computer program 32, the function of each module / unit in the foregoing device embodiments is implemented, for example figure 2 The functions of modules 201 to 205 are shown.

[0106] Exemplarily, the computer program 32 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete This application. The one or more modules / units may be a series of co...

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 is suitable for the technical field of image processing, and provides an image recognition method and device, and terminal equipment, and the method comprises the steps: obtaining a sample training set; executing feature extraction operation, and inputting sample image groups in the sample training set into a twin neural network to obtain a first feature vector and a second feature vector, wherein the first feature vector and the second feature vector are normalized feature vectors; calculating a loss value according to the first feature vector, the second feature vector, samplemarks and a cosine distance-based comparison loss function; if the loss value is greater than a preset loss threshold and the iteration frequency is less than the preset iteration frequency, updatingthe twin neural network according to the loss value, adding 1 to the iteration frequency, and returning to the feature extraction operation; and if the loss value is less than or equal to the preset loss threshold or the number of iterations is greater than or equal to the preset number of iterations, performing image recognition on to-be-processed image groups by using the twin neural network. The problem that existing conventional image recognition method is low in accuracy can be solved.

Description

Technical field [0001] This application belongs to the field of image processing technology, and in particular relates to an image recognition method, device and terminal equipment. Background technique [0002] In the current image processing field, image recognition tasks such as face recognition and pedestrian re-recognition are fine-grained image classification problems, with a large number of categories, small differences between different facial images and pedestrian images, and high recognition difficulty. [0003] The current image recognition methods still have the problem of low accuracy when dealing with these fine-grained image classification problems. People are actively exploring various image recognition solutions, hoping to narrow the distance between the image features of the same person or object, and widen the distance between the image features of different people or objects, and handle these fine-grained image classification problems with higher precision. The...

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): G06K9/62G06N3/04
CPCG06N3/045G06F18/22G06F18/214
Inventor 宋方良
Owner SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
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

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com