Surface curvature based completed local ternary pattern for texture image classification

被引:0
|
作者
Chen, Xi [1 ]
Zhang, Yunfei [1 ]
Zhou, Zaihong [2 ]
机构
[1] Guizhou Normal Univ, Sch Big Data & Comp Sci, Guiyang 550025, Peoples R China
[2] Guangdong Med Univ, Sch Biomed Engn, Zhanjiang 524023, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
image curvature; shape index; completed local ternary pattern; texture feature extraction; ROTATION-INVARIANT; FACE RECOGNITION; RETRIEVAL; GRADIENTS; FEATURES; SCALE; COLOR;
D O I
10.1504/IJBM.2023.133159
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The curvature of two-dimensional function can describe the degree of surface curvature. When an image is treated as a discrete two-dimensional function, image curvature describes the structural relationship between local pixels of the image. Local ternary pattern is an effective image texture descriptor to encode shape index based on image curvature. In this paper, the completed local ternary pattern, which contains the symbol characteristics, amplitude characteristics and central pixel characteristics of the local ternary pattern of shape index (completed local ternary pattern based on shape index, SI-CLTP) are all considered at the same time. Experiments on two texture databases and one palmprint database fully show that shape index based completed local ternary pattern is an effective image texture descriptor.
引用
收藏
页码:606 / 622
页数:18
相关论文
共 50 条
  • [1] Geometry-based Completed Local Binary Pattern for Texture Image Classification
    Wang, Wei
    Kou, Qiqi
    Zhou, Shu
    Luo, Ke
    Zhang, Lifeng
    2020 IEEE 3RD INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SIGNAL PROCESSING (ICICSP 2020), 2020, : 274 - 278
  • [2] Completed Local Ternary Pattern for Rotation Invariant Texture Classification
    Rassem, Taha H.
    Khoo, Bee Ee
    SCIENTIFIC WORLD JOURNAL, 2014,
  • [3] A New Feature-Based Wavelet Completed Local Ternary Pattern (Feat-WCLTP) for Texture Image Classification
    Shamaileh, Abeer Moh'd
    Rassem, Taha H.
    Chuin, Liew Siau
    Al Sayaydeh, Osama Nayel
    IEEE ACCESS, 2020, 8 : 28276 - 28288
  • [4] Completed Hybrid Local Binary Pattern for Texture Classification
    Yuan, Jing-Hua
    Huang, De-Shuang
    Zhu, Hao-Dong
    Gan, Yong
    PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 2050 - 2057
  • [5] Adjacent Evaluation of Completed Local Ternary Count for Texture Classification
    Sree, Ch. Sudha
    Rao, M. V. P. Chandra Sekhara
    2017 7TH IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2017, : 690 - 696
  • [6] Completed robust local binary pattern for texture classification
    Zhao, Yang
    Jia, Wei
    Hu, Rong-Xiang
    Min, Hai
    NEUROCOMPUTING, 2013, 106 : 68 - 76
  • [7] Local directional ternary pattern: A New texture descriptor for texture classification
    El Khadiri, I.
    Chahi, A.
    El Merabet, Y.
    Ruichek, Y.
    Touahni, R.
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2018, 169 : 14 - 27
  • [8] A Completed Modeling of Local Binary Pattern Operator for Texture Classification
    Guo, Zhenhua
    Zhang, Lei
    Zhang, David
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2010, 19 (06) : 1657 - 1663
  • [9] Neutrosophic set based local binary pattern for texture classification
    Alpaslan, Nuh
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 209
  • [10] A COMPLETED MULTI-SCALE LOCAL STATISTICS PATTERN FOR TEXTURE CLASSIFICATION
    Xu, Xiaochun
    Li, Bin
    Wu, Q. . M. Jonathan
    IMAGE ANALYSIS & STEREOLOGY, 2024, 43 (03) : 277 - 293