Data fusion algorithm of coupled images

doi:10.11772/j.issn.1001-9081.2018020482

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (10): 2862-2868.DOI: 10.11772/j.issn.1001-9081.2018020482

Previous Articles Next Articles

Data fusion algorithm of coupled images

REN Xiaoxu¹, LYU Liangfu¹, CUI Guangtai²

1. School of Mathematics, Tianjin University, Tianjin 300072, China;
2. School of Science, Hohai University, Nanjing Jiangsu 211100, China

Received:2018-03-09 Revised:2018-05-20 Online:2018-10-10 Published:2018-10-13
Supported by:
This work is partially supported by the Natural Science Foundation of Tianjin (15JCQNJC00200).

耦合图像间的数据融合算法

任晓旭¹, 吕良福¹, 崔广泰²

1. 天津大学数学学院, 天津 300072;
2. 河海大学理学院, 南京 211100

通讯作者: 吕良福
作者简介:任晓旭(1994-),女,河北保定人,硕士研究生,主要研究方向:机器学习、稀疏优化;吕良福(1979-),男,山东潍坊人,副教授,博士,主要研究方向:机器学习、稀疏优化;崔广泰(1996-),男,山东潍坊人,主要研究方向:机器学习、稀疏优化。
基金资助:
天津市自然科学基金资助项目（15JCQNJC00200）。

Abstract

Abstract: Coupled data fusion algorithms mainly improve estimation accuracy and explain related latent variables of the other coupled data sets by using the information of one data set. Aiming at the large number of coupled images existing in reality, based on the Coupled Matrix and Tensor Factorization-OPTimization (CMTF-OPT) algorithm in coupled data fusion, a Coupled Images Factorization-OPTimization (CIF-OPT) algorithm for coupled images was proposed. The corresponding theoretical analysis and experimental results show that the effects of coupled image fusion by CIF-OPT algorithm under different noise influence are robust, and better than those by other coupling algorithms. Particularly, the CIF-OPT algorithm can accurately restore an image of missing some data elements with its coupled image at a certain probability.

Key words: data fusion, coupled image, matrix decomposition, tensor decomposition, optimization algorithm

摘要： 耦合数据的融合算法主要通过利用其中一个数据集的信息提高对其他耦合数据集的估计精度和完善对相关潜变量的解释。针对现实中存在的大量耦合图像，基于耦合数据融合中的耦合矩阵和张量分解优化（CMTF-OPT）算法，提出一种针对耦合图像的耦合图像分解优化（CIF-OPT）算法。相应的理论分析及实验结果表明，不同噪声影响下用CIF-OPT算法进行耦合图像融合后的效果均具有鲁棒性，且融合效果优于其他耦合算法（如：CMTF-OPT算法）。特别地，针对其中缺失数据元素的图像，CIF-OPT算法可以利用与其耦合的图像，对缺失数据元素的图像进行精确的数据恢复。

关键词: 数据融合, 耦合图像, 矩阵分解, 张量分解, 优化算法

CLC Number:

TP391.1
O232

REN Xiaoxu, LYU Liangfu, CUI Guangtai. Data fusion algorithm of coupled images[J]. Journal of Computer Applications, 2018, 38(10): 2862-2868.

任晓旭, 吕良福, 崔广泰. 耦合图像间的数据融合算法[J]. 计算机应用, 2018, 38(10): 2862-2868.

References

[1] SUI J, ADALI T, YU Q, et al. A review of multivariate methods for multimodal fusion of brain imaging data[J]. Journal of Neuroscience Methods, 2012, 204(1):68-81.
[2] VEGANZONES M A, COHEN J E, FARIAS R C, et al. Nonnegative tensor CP decomposition of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5):2577-2588.
[3] SHASHUA A, LEVIN A. Linear image coding for regression and classification using the tensor-rank principle[C]//CVPR 2001:Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2001,1:42-49.
[4] BAUCKHAGE C. Robust tensor classifiers for color object recognition[C]//Proceedings of the 2007 International Conference Image Analysis and Recognition, LNCS 4633. Berlin:Springer, 2007:352-363.
[5] FARIAS R C, COHEN J E, COMON P. Exploring multimodal data fusion through joint decompositions with flexible couplings[J]. IEEE Transactions on Signal Processing, 2016, 64(18):4830-4844.
[6] BANERJEE A, BASU S, MERUGU S. Multi-way clustering on relation graphs[EB/OL].[2018-01-11]. https://www-users.cs.umn.edu/~baner029/papers/07/mrgc.pdf.
[7] ACAR E, KOLDA T G, DUNLAVY D M. All-at-once optimization for coupled matrix and tensor factorizations[EB/OL].[2018-01-11]. http://www.sandia.gov/~dmdunla/publications/AcKoDu11.pdf.
[8] ACAR E, LAWAETZ A J, RASMUSSEN M A, et al. Structure-revealing data fusion model with applications in metabolomics[C]//Proceedings of the 201335th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ:IEEE, 2013:6023-6026.
[9] CARROLL J D, CHANG J J. Analysis of individual differences in multidimensional scaling via an n-way generalization of "Eckart-Young" decomposition[J]. Psychometrika, 1970, 35(3):283-319.
[10] HARSHMAN R A. Foundations of the PARAFAC procedure:models and conditions for an "explanatory" multi-modal factor analysis[J]. UCLA Working Papers in Phonetics, 1970,16:1-84.
[11] TUCKER L R. Implications of factor analysis of three-way matrices for measurement of change[J]. Problems in Measuring Change, 1963, 15:122-137.
[12] SINGH A P, GORDON G J. Relational learning via collective matrix factorization[C]//KDD 2008:Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2008:650-658.
[13] BUCHANAN A M, FITZGIBBON A W. Damped Newton algo-rithms for matrix factorization with missing data[C]//CVPR 2005:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2005, 2:316-322.
[14] TOMASI G, BRO R. A comparison of algorithms for fitting the PARAFAC model[J]. Computational Statistics and Data Analysis, 2006, 50(7):1700-1734.
[15] SMILDE A K, WESTERHUIS J A, BOQUE R. Multiway multiblock component and covariates regression models[J]. Journal of Chemometrics, 2015, 14(3):301-331.
[16] KOLDA T G, BADER B W. Tensor decompositions and applications[C]//Proceedings of the 2003 IEEE Conference on Decision and Control. Piscataway, NJ:IEEE, 2004,1:640-645.
[17] NOCEDAL J, WRIGHT S J. Numerical Optimization[M]. Berlin:Springer, 1999:327-330.
[18] ACAR E, DUNLAVY D M, KOLDA T G. A scalable optimization approach for fitting canonical tensor decompositions[J]. Journal of Chemometrics, 2015, 25(2):67-86.

Data fusion algorithm of coupled images

耦合图像间的数据融合算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	CHEN Jun, HE Qing. Improved butterfly optimization algorithm based on cosine similarity [J]. Journal of Computer Applications, 2021, 41(9): 2668-2677.
[2]	TANG Andi, HAN Tong, XU Dengwu, XIE Lei. Path planning method of unmanned aerial vehicle based on chaos sparrow search algorithm [J]. Journal of Computer Applications, 2021, 41(7): 2128-2136.
[3]	RAN Jiamin, NI Zhiwei, PENG Peng, ZHU Xuhui. Task allocation strategy considering service quality of spatial crowdsourcing workers and its glowworm swarm optimization algorithm solution [J]. Journal of Computer Applications, 2021, 41(3): 794-802.
[4]	LU Rongxiu, CHEN Mingming, YANG Hui, ZHU Jianyong. Element component content dynamic monitoring system based on time sequence characteristics of solution images [J]. Journal of Computer Applications, 2021, 41(10): 3075-3081.
[5]	DONG Hai, WU Yao, QI Xinna. Multi-objective robust optimization design of blood supply chain network based on improved whale optimization algorithm [J]. Journal of Computer Applications, 2021, 41(10): 3063-3069.
[6]	TONG Lin, GUAN Zheng. Fuzzy granulation prediction of traffic flow based on improved whale optimization support vector machine [J]. Journal of Computer Applications, 2021, 41(10): 2919-2927.
[7]	LI Erchao, LI Kangwei. Decomposition based many-objective evolutionary algorithm based on minimum distance and aggregation strategy [J]. Journal of Computer Applications, 2021, 41(1): 22-28.
[8]	WANG Bo, LIU Liansheng, HAN Shaocheng, ZHU Shixing. Hybrid multi-objective grasshopper optimization algorithm based on fusion of multiple strategies [J]. Journal of Computer Applications, 2020, 40(9): 2670-2676.
[9]	QU Jingyi, CAO Lei, CHEN Min, DONG Liang, CAO Yexiu. CliqueNet flight delay prediction model based on clique random connection [J]. Journal of Computer Applications, 2020, 40(8): 2420-2427.
[10]	ZENG Minghua, QUAN Ke. Improved hybrid cuckoo search-based quantum-behaved particle swarm optimization algorithm for bi-level programming [J]. Journal of Computer Applications, 2020, 40(7): 1908-1912.
[11]	XIE Qi, XU Xu, CHENG Gengguo, CHEN Heping. Feature selection algorithm based on new forest optimization algorithm [J]. Journal of Computer Applications, 2020, 40(5): 1266-1271.
[12]	YANG Yunting, WANG Peng. Multi-scale quantum free particle optimization algorithm for solving travelling salesman problem [J]. Journal of Computer Applications, 2020, 40(5): 1278-1283.
[13]	ZHANG Wu, CHEN Hongmei. Hyperspectral band selection based on multi-kernelized fuzzy rough set and grasshopper optimization algorithm [J]. Journal of Computer Applications, 2020, 40(5): 1425-1430.
[14]	ZHU Changsheng, KANG Lianghe, FENG Wenfang. Stock closing price prediction algorithm using adaptive whale optimization algorithm and Elman neural network [J]. Journal of Computer Applications, 2020, 40(5): 1501-1509.
[15]	ZHANG Xiajie, ZHU Jinghua, CHEN Yang. Distributed rough set attribute reduction algorithm under Spark [J]. Journal of Computer Applications, 2020, 40(2): 518-523.