Feature fusion of dynamic visual Angle images based on grouping feature weight

Home > Archive>Volume 44, Issue 4, 2021 >144-148

Feature fusion of dynamic visual Angle images based on grouping feature weight
DOI:
                        
                    
CSTR:
                        
                    
Author:
                        Zhang Jiaqi1Zhang Jiaqi
Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University, Shanghai 200444 China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Zhang Jinyi1Zhang Jinyi
Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University, Shanghai 200444 China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site
Lou Liangliang2Lou Liangliang
Key Laboratory of Wireless Sensor Network & Communication，Shanghai Institute of Microsystem and Information Technology，Chinese Academy of Sciences，865 Changning Road, Shanghai 200050 China
Find this author on All Journals
Find this author on BaiDu
Search for this author on this site

                    
Affiliation:1.Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University, Shanghai 200444 China; 2. Key Laboratory of Wireless Sensor Network & Communication，Shanghai Institute of Microsystem and Information Technology，Chinese Academy of Sciences，865 Changning Road, Shanghai 200050 China
Clc Number:TP391
Fund Project:

Article

Figures

Metrics

Reference

Cited by

Materials

Comments

Abstract:

In order to solve the problem of low recognition accuracy, the image captured by mobile robot in the process of target recognition has multi-target interference and the target feature is limited in a single perspective. In this paper, a method of Feature fusion of dynamic visual Angle images based on grouping feature weight weights is proposed. In this method, multiple target features are weighted and grouped by progressive K-means clustering, and continuous image features are fused from dynamic perspective by LSTM network, so as to improve the accuracy of target recognition. The verification results show that the first recognition rate on market-1501 data set reaches 93.80%, and the average accuracy reaches 89.13%, with good experimental results.

Key words:grouping feature weight;feature fusion;feature extraction;target recognition;long short term memory network

Get Citation

Copy

Article Metrics

Abstract:
PDF:
HTML:
Cited by:

History

Received:
Revised:
Adopted:
Online: November 25,2024
Published:

Home

Introduction

Editorial Committee

Policy

Contact Us

中文版

Get Citation

Share

Article Metrics

History

Article QR Code