A PSO-Neural Network-Based Feature Matching Approach in Data Integration

被引：7

作者：

Wang, Yanxia ^{[1
]}

Lv, Hongwei ^{[2
]}

Chen, Xuri ^{[1
]}

Du, Qingyun ^{[2
]}

机构：

[1] Fuzhou Invest & Surveying Inst, Fuzhou 350003, Peoples R China

[2] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China

来源：

CARTOGRAPHY - MAPS CONNECTING THE WORLD | 2015年

关键词：

Particle swarm optimization; Artificial neural network; Feature matching; Data integration; PARTICLE SWARM OPTIMIZATION; MAP CONFLATION; ALGORITHM; DESIGN; SETS;

D O I：

10.1007/978-3-319-17738-0_14

中图分类号：

P9 [自然地理学]; K9 [地理];

学科分类号：

0705 ; 070501 ;

摘要：

This chapter presents a feature matching approach based on a particle swarm optimization neural network (PSONN) in data integration to identify the corresponding features in different datasets. Unlike previous probability-based feature matching using a weighted average of multiple measures calculating matching probability, the proposed approach utilizes PSONN, obtaining similarity rules of feature matching to find matched features in different datasets. The feature matching strategy utilizing bidirectional matching, two-stage matching, and feature combination is also provided for solving all types of feature matching, including 1: 0, 0: 1, 1: 1, 1:n, m: n, and m:1. The proposed approach is implemented for matching features from different datasets and is compared with a probability-based feature matching method. The experiments show that the weights of the same measures may vary for different data contexts. In addition, the results demonstrate the availability and advantages of the proposed approach in feature matching.

引用

页码：189 / 219

页数：31

共 50 条

[11] Face Recognition Using Contourlet-Based Features and Hybrid PSO-Neural Model
Darestani, Mohammad Reza Yousefi
Sheikhan, Mansour
Khademi, Maryam
2013 5TH CONFERENCE ON INFORMATION AND KNOWLEDGE TECHNOLOGY (IKT), 2013, : 181 - 186
[12] Interpolation of Missing Wind Data Based on PSO Neural Network
Yang, Zhiling
Liu, Yongqian
2010 CONFERENCE ON ENERGY STRATEGY AND TECHNOLOGY (CEST 2010), 2010, : 5 - 9
[13] Vehicle Classification Using AKAZE and Feature Matching Approach and Artificial Neural Network
Bedruz, Rhen Anjerome R.
Fernando, Arvin
Bandala, Argel
Sybingco, Edwin
Dadios, Elmer
PROCEEDINGS OF TENCON 2018 - 2018 IEEE REGION 10 CONFERENCE, 2018, : 1824 - 1827
[14] A Feature Matching Method based on the Convolutional Neural Network
Dang, Wei
Xiang, Longhai
Liu, Shan
Yang, Bo
Liu, Mingzhe
Yin, Zhengtong
Yin, Lirong
Zheng, Wenfeng
JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY, 2023, 67 (03)
[15] A Neural Network-Based Approach for Approximating Arbitrary Roots of Polynomials
Freitas, Diogo
Guerreiro Lopes, Luiz
Morgado-Dias, Fernando
MATHEMATICS, 2021, 9 (04) : 1 - 14
[16] Convolutional neural network-based PSO for lung nodule false positive reduction on CT images
Franca da Silva, Giovanni Lucca
Azevedo Valente, Thales Levi
Silva, Aristofanes Correa
de Paiva, Anselmo Cardoso
Gattass, Marcelo
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2018, 162 : 109 - 118
[17] Network Safety Evaluation based on Pso-Rbf Neural Network
Song Hai-Sheng
FIFTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2012): ALGORITHMS, PATTERN RECOGNITION AND BASIC TECHNOLOGIES, 2013, 8784
[18] FEATURE CLUSTERING FOR PSO-BASED FEATURE CONSTRUCTION ON HIGH-DIMENSIONAL DATA
Swesi, Idheba Mohamad Ali Omer
Abu Bakar, Azuraliza
JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY-MALAYSIA, 2019, 18 (04): : 439 - 472
[19] A Big Data Mining Approach of PSO-Based BP Neural Network for Financial Risk Management With IoT
Zhou, Hangjun
Sun, Guang
Fu, Sha
Liu, Jing
Zhou, Xingxing
Zhou, Jieyu
IEEE ACCESS, 2019, 7 : 154035 - 154043
[20] A neural network-based approach for user experience assessment
Amanatiadis, A.
Mitsinis, N.
Maditinos, D.
BEHAVIOUR & INFORMATION TECHNOLOGY, 2015, 34 (03) : 304 - 315

← 1 2 3 4 5 →