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基于局部补数-导数模式的光照反转和旋转不变纹理表达

辛亮亮 宋铁成 张刚 高陈强 张天骐

辛亮亮, 宋铁成, 张刚, 高陈强, 张天骐. 基于局部补数-导数模式的光照反转和旋转不变纹理表达.自动化学报, 2021, 47(4): 924-932 doi: 10.16383/j.aas.c180201
引用本文: 辛亮亮, 宋铁成, 张刚, 高陈强, 张天骐. 基于局部补数-导数模式的光照反转和旋转不变纹理表达.自动化学报, 2021, 47(4): 924-932 doi: 10.16383/j.aas.c180201
Xin Liang-Liang, Song Tie-Cheng, Zhang Gang, Gao Chen-Qiang, Zhang Tian-Qi. Illumination-inversion and rotation invariant texture representation based on local complement and derivative pattern. Acta Automatica Sinica, 2021, 47(4): 924-932 doi: 10.16383/j.aas.c180201
Citation: Xin Liang-Liang, Song Tie-Cheng, Zhang Gang, Gao Chen-Qiang, Zhang Tian-Qi. Illumination-inversion and rotation invariant texture representation based on local complement and derivative pattern. Acta Automatica Sinica, 2021, 47(4): 924-932 doi: 10.16383/j.aas.c180201

基于局部补数-导数模式的光照反转和旋转不变纹理表达

doi: 10.16383/j.aas.c180201
基金项目: 

国家自然科学基金项目 61702065

国家自然科学基金项目 61671095

国家自然科学基金项目 61571071

信号与信息处理重庆市市级重点实验室建设项目 CSTC2009CA2003

详细信息
    作者简介:

    辛亮亮  重庆邮电大学硕士研究生. 主要研究方向为纹理特征提取. E-mail: xxinliangliang@126.com

    张刚  重庆邮电大学副教授. 主要研究方向为混沌通信与微弱信号检测. E-mail: zhanggang@cqupt.edu.cn

    高陈强  重庆邮电大学教授. 主要研究方向为图像处理与计算机视觉. E-mail: gaocq@cqupt.edu.cn

    张天骐  重庆邮电大学教授. 主要研究方向为通信信号处理与多媒体信息处理. E-mail: zhangtq@cqupt.edu.cn

    通讯作者:

    宋铁成  重庆邮电大学讲师. 主要研究方向为图像处理与计算机视觉. 本文通信作者. E-mail: xyzren@yeah.net

Illumination-Inversion and Rotation Invariant Texture Representation Based on Local Complement and Derivative Pattern

Funds: 

National Natural Science Foundation of China 61702065

National Natural Science Foundation of China 61671095

National Natural Science Foundation of China 61571071

Project of Key Laboratory of Signal and Information Processing of Chongqing CSTC2009CA2003

More Information
    Author Bio:

    XIN Liang-Liang  Master student at Chongqing University of Posts and Telecommunications. His main research interest is texture feature extraction

    ZHANG Gang  Associate professor at Chongqing University of Posts and Telecommunications. His research interest covers chaotic communication and weak signal detection

    GAO Chen-Qiang  Professor at Chongqing University of Posts and Telecommunications. His research interest covers image processing and computer vision

    ZHANG Tian-Qi  Professor at Chongqing University of Posts and Telecommunications. His research interest covers communication signal processing and multimedia information processing

    Corresponding author: SONG Tie-Cheng  Lecturer at Chongqing University of Posts and Telecommunications. His research interest covers image processing and computer vision. Corresponding author of this paper
  • 摘要: 针对现有局部二值模式(Local binary pattern, LBP) 算法对光照反转变化敏感和特征描述力不足的问题, 本文提出一种基于局部补数-导数模式(Local complement and derivative pattern, LCDP) 的纹理表达方法. 其中, 局部补数模式(Local complement pattern, LCP) 用于编码原始图像空间中的近邻差分符号信息, 局部导数模式(Local derivative pattern, LDP) 用于编码不同尺度下(一阶和二阶) 高斯导数空间中的近邻差分幅值信息, 二者对光照反转和图像旋转均具有鲁棒性. 为实现对差分符号和差分幅值的联合统计, 同时维持特征的紧致性, 进一步提出基于均值采样的联合编码方案. 最后, 对联合编码的结果进行多尺度直方图特征表达. 实验表明, 该方法能够有效提高线性和非线性光照反转条件下纹理图像的分类精度.
    Recommended by Associate Editor BAI Xiang
    1)  本文责任编委 白翔
  • 图  1  真实生活场景中的光照反转现象

    Fig.  1  Inverse illumination changes in real-world scenes

    图  2  光照反转对LBP编码值的影响

    Fig.  2  The influence of inverse illumination on LBP codes

    图  3  本文方法构建LCDP描述符的框架

    Fig.  3  The framework of the proposed method to construct LCDP descriptor

    图  4  均值采样示意图$ (r, P) = (3, 24) $

    Fig.  4  The diagram of mean sampling with $ (r, P) = (3, 24) $

    图  5  线性光照反转条件下采样半径和多尺度滤波对LCDP分类性能的影响

    Fig.  5  The influence of sampling radius and filtering scale on the classification performance of LCDP under linear inverse illumination conditions

    图  6  LCP、LDP和LCDP在线性光照反转条件下的分类精度

    Fig.  6  Classification accuracies of LCP, LDP and LCDP under linear inverse illumination conditions

    表  1  不同方法在线性光照反转条件下的分类精度(%)

    Table  1  Classification accuracies (%) of different methods under linear inverse illumination conditions

    方法 Outex CUReT KTH-TIPS
    TC10 TC12
    tl84 horizon
    LBP[2] 39.98 39.34 39.63 43.47 47.07
    LTP[13] 21.37 37.99 38.91 44.71 54.46
    CLBP[16] 24.49 22.12 22.72 34.33 43.15
    CLBC[17] 20.20 18.68 18.96 31.02 41.54
    LETRIST[18] 32.97 32.37 34.89 43.68 53.75
    jcLSFP[19] 33.78 35.49 38.40 49.04 54.54
    NRLBP[20] 42.34 42.49 45.26 83.21 87.88
    LGP[21] 94.58 78.47 76.65 89.73 88.85
    GLBP[22] 93.39 90.43 89.35 88.21 89.12
    SLGP[23] 97.79 84.17 83.82 94.87 93.83
    SIFT[29] 19.39 14.43 13.75 13.49 27.95
    LCP 71.72 62.57 60.56 61.30 68.68
    LDP 97.24 94.35 94.58 92.51 94.31
    LCDP 99.69 95.63 96.18 96.52 95.68
    下载: 导出CSV

    表  2  不同方法在非线性光照反转条件下的分类精度(%)

    Table  2  Classification accuracies (%) of different methods under nonlinear inverse illumination conditions

    方法 Outex CUReT KTH-TIPS
    TC10 TC12
    tl84 horizon
    LBP[2] 40.63 38.91 39.40 43.18 46.93
    LTP[13] 36.56 35.32 37.08 43.72 41.66
    CLBP[16] 22.99 23.15 23.87 35.59 44.93
    CLBC[17] 18.29 18.80 19.75 33.16 42.34
    LETRIST[18] 4.17 4.17 4.17 2.30 12.24
    jcLSFP[19] 33.88 35.56 37.85 49.45 53.07
    NRLBP[20] 42.32 42.94 45.32 69.02 87.73
    LGP[21] 91.27 71.08 70.49 85.18 87.49
    GLBP[22] 92.79 89.32 88.70 87.90 87.66
    SLGP[23] 95.60 75.49 73.63 91.23 90.82
    SIFT[29] 11.94 13.61 13.69 17.03 29.88
    LCP 71.57 62.19 60.64 61.39 68.51
    LDP 96.43 92.77 92.75 90.80 92.05
    LCDP 99.61 95.09 95.42 95.81 94.54
    下载: 导出CSV

    表  3  不同方法在原始数据库上的分类精度(%)

    Table  3  Classiflcation accuracies (%) of difierent methods on the original databases

    方法 Outex CUReT KTH-TIPS
    TC10 TC12
    tl84 horizon
    LBP[2] 97.16 88.96 83.96 93.52 92.70
    LTP[13] 98.65 92.69 89.86 94.46 94.36
    CLBP[16] 99.17 95.23 95.58 96.94 96.50
    CLBC[17] 99.04 94.10 95.14 96.78 96.39
    LETRIST[18] 100.00 99.81 100.00 98.52 98.80
    jcLSFP[19] 100.00 99.77 99.93 98.20 98.72
    NRLBP[20] 45.96 48.33 50.95 83.08 87.36
    LGP[21] 94.58 78.47 76.65 89.86 89.83
    GLBP[22] 93.20 90.37 89.26 88.44 87.63
    SLGP[23] 97.79 84.17 83.82 94.83 93.71
    SIFT[29] 48.61 52.75 53.18 78.12 93.37
    LCP 71.72 62.57 60.56 61.41 68.56
    LDP 97.24 94.35 94.58 92.37 94.52
    LCDP 99.69 95.63 96.18 96.60 95.49
    下载: 导出CSV

    表  4  不同方法在KTH-TIPS数据库上提取单个图像描述符所需的平均时间(秒)

    Table  4  Average time (second) for different methods to extract one image descriptor on the KTH-TIPS database

    方法 时间 方法 时间 方法 时间
    LBP 0.040 LETRIST 0.052 GLBP 0.914
    LTP 0.047 jcLSFP 0.077 SLGP 0.084
    CLBP 0.063 NRLBP 0.010 SIFT 0.087
    CLBC 0.056 LGP 0.041 LCDP 0.139
    下载: 导出CSV
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  • 收稿日期:  2018-04-10
  • 录用日期:  2018-08-01
  • 刊出日期:  2021-04-23

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