Optimized clustering method for spectral reflectance recovery

被引:5
|
作者
Xiong, Yifan [1 ]
Wu, Guangyuan [1 ]
Li, Xiaozhou [2 ]
Wang, Xin [1 ]
机构
[1] Qilu Univ Technol, Shandong Acad Sci, Fac Light Ind, Jinan, Peoples R China
[2] Qilu Univ Technol, Shandong Acad Sci, State Key Lab Biobased Mat & Green Papermaking, Jinan, Peoples R China
来源
FRONTIERS IN PSYCHOLOGY | 2022年 / 13卷
关键词
spectral recovery; dynamic partitional clustering; color space; camera responses; spectral reflectance; RECONSTRUCTION; IMAGE;
D O I
10.3389/fpsyg.2022.1051286
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
An optimized method based on dynamic partitional clustering was proposed for the recovery of spectral reflectance from camera response values. The proposed method produced dynamic clustering subspaces using a combination of dynamic and static clustering, which determined each testing sample as a priori clustering center to obtain the clustering subspace by competition. The Euclidean distance weighted and polynomial expansion models in the clustering subspace were adaptively applied to improve the accuracy of spectral recovery. The experimental results demonstrated that the proposed method outperformed existing methods in spectral and colorimetric accuracy and presented the effectiveness and robustness of spectral recovery accuracy under different color spaces.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Research on the Optimized Clustering Method Based on CFSFDP
    Sun, Longlong
    Jiang, Ming
    2019 34RD YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC), 2019, : 28 - 33
  • [22] An optimized FCM method for electric load clustering
    Li, Cailing
    Wang, Jin
    Li, Xinran
    2008 THIRD INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES, VOLS 1-6, 2008, : 882 - 886
  • [23] An improved method of linear spectral clustering
    Qiao, Nianzu
    Di, Lamei
    MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (01) : 1287 - 1311
  • [24] An improved method of linear spectral clustering
    Nianzu Qiao
    Lamei Di
    Multimedia Tools and Applications, 2022, 81 : 1287 - 1311
  • [25] Spectral Clustering Method and Its Application
    Zhang, Li
    Yu, Haibin
    MATERIALS, INFORMATION, MECHANICAL, ELECTRONIC AND COMPUTER ENGINEERING (MIMECE 2016), 2016, : 44 - 49
  • [26] Iterative Spectral Method for Alternative Clustering
    Wu, Chieh
    Ioannidis, Stratis
    Sznaier, Mario
    Li, Xiangyu
    Kaeli, David
    Dy, Jennifer G.
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 84, 2018, 84
  • [27] A spectral clustering method for microarray data
    Tritchler, D
    Fallah, S
    Beyene, J
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2005, 49 (01) : 63 - 76
  • [28] Multi-Source Clustering based on spectral recovery
    Yin, Hongwei
    Li, Fanzhang
    Zhang, Li
    2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2018, : 231 - 236
  • [29] An Unknown Protocol Clustering Analysis Method Based on Spectral Clustering
    Ni, Lulin
    Shi, Yu
    Luo, Jie
    Ji, Qingbing
    2021 IEEE 9TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION AND NETWORKS (ICICN 2021), 2021, : 445 - 449
  • [30] Use of spectral sensitivity variability in reflectance recovery from colorimetric information
    Amiri, Morteza Maali
    Fairchild, Mark D.
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2017, 34 (07) : 1224 - 1235