Footprint image retrieval method based on multi-scale local attention enhancement network

An image retrieval and network enhancement technology, applied in neural learning methods, biological neural network models, digital data information retrieval, etc., can solve the problems of slow speed and low accuracy of footprint image retrieval, and achieve the goal of reducing gradient disappearance, fast transfer and Conversion, effects that improve speed and accuracy

Active Publication Date: 2021-08-06
ANHUI UNIVERSITY +2
View PDF5 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the defects of low accuracy and slow speed of footprint image retrieval in the prior art, and provide a footprint image retrieval method based on multi-scale local attention enhancement network to solve the above problems

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
  • Footprint image retrieval method based on multi-scale local attention enhancement network
  • Footprint image retrieval method based on multi-scale local attention enhancement network
  • Footprint image retrieval method based on multi-scale local attention enhancement network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0059] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0060] Such as figure 1 As shown, a kind of footprint image retrieval method based on multi-scale local attention enhancement network of the present invention comprises the following steps:

[0061] The first step is the acquisition and preprocessing of training samples: obtain the footprint image dataset, filter and denoise each footprint image, and select a reasonable threshold for binarization to obtain the processed footprint image dataset.

[0062] In the laboratory session, the collection test object puts the left foot on the footprint printing collector and stands for about 3 seconds to complete the collection of a footprint image. The collector will automatically transmit the image to the computer for display and storage, and ...

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 footprint image retrieval method based on a multi-scale local attention enhancement network. Compared with the prior art, the defects that footprint image retrieval is low in accuracy and slow in speed are overcome. The method comprises the following steps: obtaining and preprocessing a training sample; constructing a footprint image retrieval model; training a footprint image retrieval model; obtaining a footprint image to be retrieved; and obtaining a footprint image retrieval result. According to the method, more effective fine-grained features can be extracted, the feature representation capability of the image is further mined, and the footprint image retrieval speed and accuracy are improved.

Description

technical field [0001] The invention relates to the technical field of image retrieval, in particular to a footprint image retrieval method based on a multi-scale local attention enhancement network. Background technique [0002] In recent years, in the field of biometrics, identification technologies including face, iris, fingerprint, gait, etc. have developed rapidly. Among many identification methods, footprint recognition is gradually becoming a research hotspot because of the uniqueness of individual walking postures, so it is concealed, difficult to camouflage and non-invasive. Studies have shown that because each person's footprint has its own characteristics, it is unique. In the past footprint recognition work, most of them relied on the knowledge and experience of experts, and there was often a lot of subjectivity, and there was still a certain gap between automatic comparison and recognition of footprints. [0003] The traditional image retrieval method is to se...

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): G06F16/583G06N3/04G06N3/08
CPCG06F16/583G06N3/084G06N3/045
Inventor 朱明江畅于小勇唐俊王年宫臣张艳鲍文霞骆刚
Owner ANHUI UNIVERSITY
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