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基于分层基因优选多特征融合的图像材质属性标注

张红斌 邱蝶蝶 邬任重 蒋子良 武晋鹏 姬东鸿

张红斌, 邱蝶蝶, 邬任重, 蒋子良, 武晋鹏, 姬东鸿.基于分层基因优选多特征融合的图像材质属性标注.自动化学报, 2020, 46(10): 2191-2213 doi: 10.16383/j.aas.c190012
引用本文: 张红斌, 邱蝶蝶, 邬任重, 蒋子良, 武晋鹏, 姬东鸿.基于分层基因优选多特征融合的图像材质属性标注.自动化学报, 2020, 46(10): 2191-2213 doi: 10.16383/j.aas.c190012
Zhang Hong-Bin, Qiu Die-Die, Wu Ren-Zhong, Jiang Zi-Liang, Wu Jin-Peng, Ji Dong-Hong. Stratified gene selection multi-feature fusion for image material attribute annotation. Acta Automatica Sinica, 2020, 46(10): 2191-2213 doi: 10.16383/j.aas.c190012
Citation: Zhang Hong-Bin, Qiu Die-Die, Wu Ren-Zhong, Jiang Zi-Liang, Wu Jin-Peng, Ji Dong-Hong. Stratified gene selection multi-feature fusion for image material attribute annotation. Acta Automatica Sinica, 2020, 46(10): 2191-2213 doi: 10.16383/j.aas.c190012

基于分层基因优选多特征融合的图像材质属性标注

doi: 10.16383/j.aas.c190012
基金项目: 

国家自然科学基金 61762038

国家自然科学基金 61861016

教育部人文社会科学研究一般项目 20YJAZH1142

江西省自然科学基金 20202BABL202044

江西省科技厅重点研发计划 20171BBG70093

江西省科技厅重点研发计划 20192BBE50071

江西省教育厅科学技术研究项目 GJJ190323

详细信息
    作者简介:

    邱蝶蝶  华东交通大学软件学院硕士研究生.主要研究方向为图像理解, 计算机视觉, 机器学习.
    E-mail: diedie888888@163.com

    邬任重   华东交通大学软件学院硕士研究生.主要研究方向为肿瘤图像识别, 计算机视觉, 机器学习.
    E-mail: renchw5@163.com

    蒋子良  华东交通大学软件学院硕士研究生.主要研究方向为肿瘤图像识别, 图像内容生成, 机器学习.
    E-mail: a320jzlnf430a@163.com

    武晋鹏  华东交通大学软件学院硕士研究生.主要研究方向为问答系统, 自然语言处理, 机器学习.
    E-mail: wujinpeng920421@163.com

    姬东鸿  武汉大学国家网络安全学院教授.主要研究方向为舆情分析, 自然语言处理, 机器学习.
    E-mail: dhji@whu.edu.cn

    通讯作者:

    张红斌   华东交通大学软件学院副教授, 主要研究方向为计算机视觉, 自然语言处理, 推荐系统.本文通信作者.
    E-mail: zhanghongbin@whu.edu.cn

  • 本文责任编委 黎铭

Stratified Gene Selection Multi-Feature Fusion for Image Material Attribute Annotation

Funds: 

National Natural Science Foundation of China 61762038

National Natural Science Foundation of China 61861016

Humanity and Social Science Foundation of the Ministry of Education 20YJAZH1142

Natural Science Foundation of Jiangxi Province 20202BABL202044

Key Research and Development Plan of Jiangxi Provincial Science and Technology Department 20171BBG70093

Key Research and Development Plan of Jiangxi Provincial Science and Technology Department 20192BBE50071

Science and Technology Projects of Jiangxi Provincial Department of Education GJJ190323

More Information
    Author Bio:

    QIU Die-Die   Master student at the Software School, East China Jiaotong University. Her research interest covers image understanding, computer vision, and machine learning

    WU Ren-Zhong    Master student at the Software School, East China Jiaotong University. Her research interest covers tumor image recognition, computer vision, and machine learning

    JIANG Zi-Liang   Master student at the Software School, East China Jiaotong University. His research interest covers tumor image recognition, image captioning, and machine learning

    WU Jin-Peng   Master student at the Software School, East China Jiaotong University. His research interest covers question answering, natural language processing, and machine learning

    JI Dong-Hong  Professor at the School of Cyber Science and Engineering, Wuhan University. His research interest covers public opinion analysis, natural language processing, and machine learning

    Corresponding author: ZHANG Hong-Bin  Associate professor at the Software School, East China Jiaotong University. His research interest covers computer vision, natural language processing, and recommendation system. Corresponding author of this paper
  • Recommended by Associate Editor LI Ming
  • 摘要: 图像材质属性标注在电商平台、机器人视觉、工业检测等领域都具有广阔的应用前景.准确利用特征间的互补性及分类模型的决策能力是提升标注性能的关键.提出分层基因优选多特征融合(Stratified gene selection multi-feature fusion, SGSMFF)算法:提取图像传统及深度学习特征; 采用分类模型计算特征预估概率; 改进有效区域基因优选(Effective range based gene selection, ERGS)算法, 并在其中融入分层先验信息(Stratified priori information, SPI), 逐层、动态地为预估概率计算ERGS权重; 池化预估概率并做ERGS加权, 实现多特征融合.在MattrSet和Fabric两个数据集上完成实验, 结果表明: SGSMFF算法中可加入任意分类模型, 并实现多特征融合; 平均值池化方法、分层先验信息所提供的难分样本信息、"S + G + L"及"S + V"特征组合等均有助于改善材质属性标注性能.在上述两个数据集上, SGSMFF算法的精准度较最强基线分别提升18.70%、15.60%.
    Recommended by Associate Editor LI Ming
    1)  本文责任编委 黎铭
  • 图  1  基于t-SNE的MattrSet数据集样本可视化结果(均为调参后的最佳效果)

    Fig.  1  Visualization results of MattrSet dataset based on t-SNE(the best results are obtained after parameters tuning)

    图  2  基于t-SNE的Fabric数据集样本可视化结果(均为调参后的最佳效果)

    Fig.  2  Visualization results of Fabric dataset based on t-SNE(the best results are obtained after parameters tuning)

    图  3  基于SGSMFF算法的材质属性标注模型(以MattrSet数据集为例, p表示Pu材质、p*表示Polyester材质、c表示Canvas材质、n表示Nylon材质. "1"表示正例, "0"表示负例)

    Fig.  3  The proposed material attribute annotation model based on the SGSMFF algorithm(MattrSet is used as example, p, p*, c, n, "1", and "0" represent Pu, Polyester, Canvas, Nylon, positive, and negative examples, respectively )

    图  4  面向不同的SGS模型或不同特征组合的精准度均值比较

    (a)和(b)是不同模型, (c)和(d)是不同特征组合

    Fig.  4  The mean accuracy comparisons of different SGS models or feature combinations

    (a) and (b) are different models, (c) and (d) are different feature combinations

    表  1  各类材质的t-SNE代价值(针对不同数据集, 每列最小值如1.3079等所示)

    Table  1  The t-SNE cost value of different material (for different dataset, the minimum value of each column is shown as 1.3079 etc.)

    数据集 材质 t-SNE代价值
    Gist SIFT LBP VGG 代价均值
    MattrSet Pu 1.3079 1.4615 0.7735 0.9142 1.1143
    Canvas 1.4517 1.7962 0.8653 0.9660 1.2698
    Nylon 1.4077 1.7227 0.8333 0.9360 1.2249
    Polyester 1.3948 1.7285 0.8318 0.9982 1.2383
    Fabrc Cotton 1.0282 1.2109 1.2102 0.8974 1.0867
    Denim 0.4405 0.9569 0.5581 0.4354 0.5977
    Fleece 0.2267 0.5844 0.1583 0.1219 0.2728
    Nylon 0.2219 0.1730 0.2105 0.1480 0.1884
    Polyester 0.7151 0.9591 0.7243 0.5471 0.7364
    Silk 0.1852 0.3642 0.1944 0.2078 0.2379
    Terrycloth 0.2441 0.4616 0.3116 0.1457 0.2907
    Viscose 0.2319 0.5017 0.2818 0.1035 0.2797
    Wool 0.4072 0.6868 0.4417 0.2565 0.4480
    下载: 导出CSV

    表  2  SGS算法中的参数设置

    Table  2  parameter settings of the proposed SGS algorithm

    参数 $ T $ $CM $ ${y_i}$ $F$ $C $ D ${x_i}$ l N n k d cc cm \
    意义 图像数据集合 分类模型集合 图像样本标签 图像特征集合 材质属性标签集合 特征组合集合 图像样本 材质属性标签数 样本总数 特征总数 特征$fea{t_z}$的维度 特征组合总数 组合中的特征数 分类模型数量 \
    参数值 $\left\{ \begin{array}{*{35}{l}} \begin{align} & \left( {{x}_{1}},{{y}_{1}} \right),\cdots , \\ & \left( {{x}_{N}},{{y}_{N}} \right) \\ \end{align} \\ \end{array} \right\}$ $\left\{ \begin{array}{*{35}{l}} \begin{align} & Classifie{{r}_{1}},\cdots , \\ & Classifie{{r}_{cm}} \\ \end{align} \\ \end{array} \right\}$ ${y_i} \subseteq C$ $\begin{align} & \left\{ fea{{t}_{1}},fea{{t}_{2}},\cdots ,fea{{t}_{n}} \right\} \\ & fea{{t}_{z}}\subseteq {{\bf{R}}^{k}},z\in \left\{ 1,\cdots ,n \right\} \\ \end{align}$ $C =\left\{ {{c_1}, {c_2}, \cdots, {c_l}} \right\}$ $\begin{align} & \left\{ feat\_com{{b}_{1}},\cdots ,feat\_com{{b}_{d}} \right\} \\ & feat\_com{{b}_{i}}=\left\{ fea{{t}_{1}},\cdots ,fea{{t}_{cc}} \right\} \\ \end{align}$ \ MattrSet:4
    Fabric:9
    MattrSet:11021
    Fabric: 5064
    4 Gist: 512
    LBP: 1180
    SIFT: 800
    VGG16: 1000
    11 单类别的特征: 1
    两种特征融合: 2
    两种特征融合: 3
    两种特征融合: 4
    7 \
    下载: 导出CSV

    表  4  MattrSet数据集上, GS类模型精准度及相对基本模型的Accuracy变化(每列最优值如46.20等表示, 单位: %)

    Table  4  The accuracies of GS models and the corresponding accuracy variations compared to the basic models in the MattrSet dataset (The optimal value of each column is expressed as 46.20, etc., unit: %)

    特征 GS类模型的Accuracy
    GS-DT GS-GBDT GS-KNN GS-LR GS-NB GS-RF GS-XGBoost
    S + G + L + V 42.33 59.83 54.89 54.57 49.12 59.14 61.23
    S + G + L 46.20 65.13 62.10 58.41 44.87 61.70 67.67
    S + G + V 42.40 57.67 51.52 50.72 49.25 57.47 59.27
    S + L + V 42.35 57.45 51.33 53.04 39.23 57.29 58.94
    L + G + V 42.42 59.09 54.37 52.59 41.66 58.98 59.87
    S + G 45.31 62.48 58.32 53.22 49.61 60.14 64.39
    S + V 42.49 53.98 41.76 46.36 47.85 55.13 56.54
    S + L 37.12 62.95 57.56 59.45 46.05 58.92 63.75
    L + G 45.65 63.79 60.90 57.34 40.88 61.75 65.08
    L + V 42.26 56.20 50.19 50.43 40.37 56.40 57.52
    G + V 42.28 56.40 50.10 47.00 43.49 56.69 57.63
    $\Delta \rm{Accuracy}_\rm{M1}$ 1.45 2.99 3.52 5.25 3.01 3.36 2.97
    $\Delta \rm{Accuracy}_\rm{M2}$ 0.96 3.85 5.77 3.61 1.25 1.99 4.74
    下载: 导出CSV

    表  3  基本分类模型的标注精准度(各数据集每列最优值如45.24等表示, 单位: %

    Table  3  The accuracy of basic model (for each dataset, the optimal value of each column is expressed as 45.24, etc, unit: %)

    数据集 特征 基本分类模型的Accuracy
    DT GBDT KNN LR NB RF XGBoost
    MattrSet L 43.67 61.28 56.33 55.84 40.86 59.76 62.93
    S 34.40 52.41 43.73 50.48 48.36 47.96 52.90
    G 45.24 60.34 56.05 49.19 43.27 59.32 61.99
    V 42.11 52.17 45.45 35.52 34.51 53.55 54.64
    Fabric L 47.31 70.02 68.17 69.15 27.17 62.40 70.38
    S 67.10 79.66 37.84 80.85 56.60 75.79 82.03
    G 51.90 70.70 71.37 55.63 51.80 68.33 73.42
    V 49.45 65.01 57.46 58.10 46.88 64.34 66.59
    下载: 导出CSV

    表  5  Fabric数据集上, GS类模型精准度及其相对基本模型的Accuracy变化(每列最优值如79.98等表示, 单位: %)

    Table  5  The accuracies of GS models and the corresponding accuracy variations compared to the basic models in the Fabric dataset (The optimal value of each column is expressed as 79.98, etc., unit: %)

    特征 GS类模型的Accuracy
    GS-DT GS-GBDT GS-KNN GS-LR GS-NB GS-RF GS-XGBoost
    S + G + L + V 58.93 73.46 68.84 68.29 45.06 69.19 78.75
    S + G + L 65.64 75.25 62.56 74.12 39.69 69.67 80.57
    S + G + V 49.45 71.96 66.90 66.00 48.82 70.85 72.71
    S + L + V 47.95 72.12 67.58 69.08 43.84 68.48 72.71
    L + G + V 47.43 72.43 71.09 65.17 42.54 68.17 72.24
    S + G 60.35 79.98 57.66 77.76 57.42 76.58 81.95
    S + V 49.45 69.91 59.28 66.94 48.54 70.02 70.62
    S + L 64.69 76.26 41.00 78.28 31.87 71.17 81.71
    L + G 48.74 71.64 73.74 66.71 27.29 66.00 73.54
    L + V 47.47 69.31 66.63 65.44 42.54 64.34 70.38
    G + V 49.45 67.73 63.78 59.64 47.47 67.58 69.04
    $\Delta \rm{Accuracy}_\rm{F1}$ -0.34 1.38 4.84 2.92 -2.42 1.56 1.82
    $\Delta \rm{Accuracy}_\rm{F2}$ -1.46 0.32 2.37 -2.57 0.82 0.79 -0.08
    下载: 导出CSV

    表  6  MattrSet数据集中SGS_MAX类模型相对GS类模型的Accuracy变化值$ \Delta {\rm{Accurac}}{{\rm{y}}_{{\rm{M3}}}}$ (性能衰减如-0.20所示, 单位: %)

    Table  6  The accuracy variations of the SGS_MAX model compared to the GS model in the MattrSet dataset: $\Delta {\rm{Accurac}}{{\rm{y}}_{{\rm{M3}}}}$ (The performance degradation indicators are marked in -0.20, unit: %)

    ${\rm{SP}}{{\rm{I}}_{\rm{M}}}$ SGS模型 $\Delta \text{Accurac}{{\text{y}}_{\text{M3}}}\text{=Accurac}{{\text{y}}_{\text{SGS }\!\!\_\!\!\text{ }}}_{\text{MAX}}\text{-Accurac}{{\text{y}}_{\text{GS}}} $
    all S + G + L S + G + V S + L + V L + G +V S + G S + V S + L L + G L + V G + V ${\rm{Av}}{{\rm{g}}_{{\rm{model}}}}$
    pcp*n
    SPIM-1
    SPIM-3
    SPIM-4
    DT 14.27 10.02 14.11 14.19 14.21 12.98 14.00 14.56 13.58 14.85 14.68 13.77
    GBDT 12.81 12.51 13.29 13.71 12.30 13.23 14.69 13.92 11.29 13.27 13.29 13.12
    KNN 12.45 8.93 13.01 13.16 12.34 10.60 20.50 10.46 9.84 13.83 14.23 12.67
    LR 22.96 17.03 24.25 24.29 23.83 20.06 27.81 16.04 14.89 25.06 26.72 22.09
    NB 16.21 16.61 20.44 22.00 23.92 11.49 21.65 12.18 16.17 16.83 20.53 18.00
    RF 14.81 16.84 14.94 15.17 13.83 16.77 15.08 19.21 14.89 14.16 14.34 15.46
    XGBoost 12.00 10.15 12.47 12.76 12.34 11.34 12.95 13.83 10.87 12.89 12.89 12.23
    pnp*c
    SPIM-2
    SPIM-5
    DT 14.89 10.05 14.03 14.28 15.63 11.32 12.09 15.30 13.98 15.16 14.57 13.75
    GBDT 9.71 7.75 10.76 11.00 10.00 9.20 12.40 8.22 9.18 11.45 11.53 10.11
    KNN 9.62 6.15 11.34 11.35 10.40 8.41 18.47 8.13 9.19 12.02 12.54 10.69
    LR 5.75 7.54 8.29 7.03 7.06 8.86 11.11 9.33 9.73 7.95 10.89 8.50
    NB 6.15 15.10 3.65 15.43 13.87 7.41 3.85 12.98 13.43 14.09 4.47 10.04
    RF 8.22 8.04 9.33 9.35 9.02 8.09 10.87 7.01 9.28 10.76 10.44 9.13
    XGBoost 9.00 6.48 10.31 10.33 10.54 8.64 10.79 8.46 9.62 11.28 11.40 9.71
    p*ncp
    SPIM-6
    DT 10.98 7.08 10.71 10.58 10.62 8.95 8.64 11.06 9.99 10.20 10.05 9.90
    GBDT 3.97 3.03 4.08 4.41 4.64 3.18 4.78 2.38 5.10 5.52 5.30 4.22
    KNN 3.31 2.83 4.48 5.18 4.13 5.27 10.92 5.46 5.10 6.23 5.54 5.31
    LR ${\bf{-0.20}}$ 0.89 0.00 0.33 ${\bf{-0.40}}$ 0.71 0.00 1.83 2.43 ${\bf{-0.20}} $ 0.01 0.49
    NB 3.59 13.89 2.14 8.17 2.43 10.76 4.12 13.74 10.62 2.65 0.00 6.56
    RF 2.27 1.51 2.81 2.78 3.34 1.43 3.57 0.85 3.27 4.32 4.21 2.76
    XGBoost 4.66 2.69 4.81 5.45 5.77 3.63 6.08 3.12 5.24 6.58 6.39 4.95
    p*pcn
    SPIM-7
    DT 15.50 9.71 15.31 15.65 15.05 13.76 14.73 15.88 14.85 15.90 15.92 14.75
    GBDT 8.79 7.08 9.22 9.99 9.76 7.75 16.25 8.19 8.82 11.20 10.60 9.79
    KNN 8.68 5.50 10.18 10.22 9.73 8.28 17.25 6.83 8.11 12.05 12.09 9.90
    LR 6.42 7.79 8.47 7.35 7.77 9.48 11.15 8.15 9.79 8.67 11.10 8.74
    NB 10.76 17.81 6.39 17.91 13.36 11.22 8.39 14.47 17.15 15.70 4.50 12.51
    RF 7.79 6.30 8.49 9.09 8.62 6.61 9.78 6.57 7.57 10.24 9.66 8.25
    XGBoost 9.02 6.55 9.60 10.22 9.92 7.49 10.57 7.99 8.25 11.19 10.91 9.25
    cpp*n
    SPIM-8
    DT 14.32 11.16 14.25 14.52 14.29 13.34 14.27 15.09 13.11 14.23 15.10 13.97
    GBDT 13.89 12.09 15.30 15.07 13.72 13.61 16.07 13.82 11.00 14.38 15.05 14.00
    KNN 13.31 9.96 14.19 14.69 13.48 10.42 19.92 11.69 10.35 14.40 15.70 13.46
    LR 22.94 17.77 23.78 23.36 22.81 21.22 26.76 16.41 15.60 23.61 24.94 21.75
    NB 18.71 19.44 22.45 24.18 22.96 15.25 23.32 15.78 16.99 24.22 35.93 21.75
    RF 16.08 16.44 16.45 16.43 15.21 16.33 16.72 19.10 15.00 15.61 15.86 16.29
    XGBoost 12.89 9.60 13.77 13.72 13.03 11.76 13.89 13.81 10.87 13.55 13.89 12.80
    npcp*
    SPIM-9
    DT 15.30 12.20 14.67 14.70 14.90 13.25 13.94 19.79 14.12 14.86 14.99 14.79
    GBDT 9.33 6.75 10.47 10.89 9.80 8.60 12.33 7.24 8.82 11.02 11.38 9.69
    KNN 9.71 6.13 11.40 11.33 10.25 8.17 18.31 8.84 8.80 11.71 12.82 10.68
    LR 6.06 6.57 8.31 7.32 4.08 8.61 11.15 8.04 8.75 8.40 10.98 8.02
    NB 6.59 14.25 4.19 17.84 15.01 7.81 4.86 10.22 14.05 14.10 4.58 10.32
    RF 7.90 6.64 9.31 9.27 8.64 7.59 10.60 6.85 9.03 10.22 10.18 8.75
    XGBoost 8.71 5.83 10.31 10.40 10.22 8.09 11.00 7.66 9.16 11.04 11.15 9.42
    $\rm{Avg}_\rm{Feat}$ 10.48 9.54 11.09 12.26 11.49 10.02 12.99 10.73 10.66 12.27 12.18 /
    下载: 导出CSV

    表  7  Fabric数据集中SGS_MAX类模型相对GS类模型的Accuracy变化值$ \Delta \rm{Accuracy}_\rm{F3}$ (性能衰减如$ \bf{{-3.47}}$所示, 单位: %)

    Table  7  The accuracy variations of the SGS_MAX model compared to the GS model in the Fabric dataset: $\Delta \rm{Accuracy}_\rm{F3}$ (The performance degradation indicators are marked in ${\bf{-3.47}}$, unit: %)

    $\text{SP}{{\text{I}}_{\text{F}}}$ SGS模型 $\Delta \text{Accurac}{{\text{y}}_{\text{F3}}}\text{=Accurac}{{\text{y}}_{\text{SGS }\!\!\_\!\!\text{ MAX}}}\text{-}\text{Accurac}{{\text{y}}_{\text{GS}}}$
    all S + G + L S + G + V S + L + V L + G + V S + G S + V S + L L + G L + V G + V $\text{Av}{{\text{g}}_{\text{model}}}$
    SPIF-1 DT 8.01 6.32 16.62 18.40 13.00 5.80 36.41 9.24 39.14 11.22 10.86 15.91
    GBDT 15.36 15.31 16.35 15.99 13.98 11.25 16.78 14.02 15.80 15.92 17.06 15.26
    KNN 6.79 0.59 8.34 0.63 13.82 1.98 6.30 3.19 12.08 12.36 17.97 7.64
    LR 18.87 15.97 20.18 19.07 18.36 11.73 20.54 12.79 15.83 18.37 20.85 17.51
    NB 33.47 24.49 29.70 30.65 27.92 15.41 28.71 35.55 23.46 27.68 27.57 27.69
    RF 18.33 18.72 16.55 17.74 16.86 12.99 15.45 16.55 19.19 18.60 16.82 17.07
    XGBoost 11.26 11.25 16.86 15.88 15.48 10.27 15.24 10.00 15.64 15.32 17.18 14.03
    SPIF-2 DT 7.74 5.57 15.56 18.24 15.88 8.05 17.18 9.36 12.16 14.10 12.24 12.37
    GBDT 0.87 3.23 1.70 1.73 ${\bf{ -1.18}} $ 1.10 4.46 4.86 1.31 0.91 2.77 1.98
    KNN 6.24 19.00 5.61 3.63 2.33 22.55 9.91 39.45 5.64 2.37 7.19 11.27
    LR 0.94 4.12 0.82 2.05 ${\bf{-0.52}} $ 2.02 4.78 4.42 1.73 1.28 1.77 2.13
    NB 6.32 29.15 1.02 6.75 6.43 19.99 3.79 45.58 32.86 6.59 1.27 14.52
    RF ${\bf{-17.81}}$ 5.09 0.79 2.22 0.43 1.38 2.61 4.15 1.65 2.96 1.38 0.44
    XGBoost $ {\bf{-3.47}} $ ${\bf{-0.16}} $ 2.09 1.30 0.75 1.42 4.22 0.83 1.66 1.22 3.31 1.20
    $ \rm{Avg}_\rm{Feat}$ 8.07 11.33 10.87 11.02 10.25 9.00 13.31 15.00 14.15 10.64 11.30 /
    下载: 导出CSV

    表  8  MattrSet数据集中, SGS_AVG类模型相对SGS_MAX类模型Accuracy变化值$ \Delta \rm{Accuracy}_\rm{M4}$ (性能衰减用${\bf{-3.82}}$表示, 单位: %)

    Table  8  The accuracy variations of the SGS_AVG model compared to the SGS_MAX model in the MattrSet dataset: $ \Delta \rm{Accuracy}_\rm{M4}$ (The performance degradation indicators are marked in ${\bf{-3.82}}$, unit: %)

    SPIM} SGS模型 $\Delta \rm{Accuracy}_\rm{M4}=\rm{Accuracy}_\rm{SGS_AVG}\, -\, \rm{Accuracy}_\rm{SGS_MAX}$
    all S + G + L S + G + V S + L + V L + G + V S + G S + V S + L L + G L + V G + V $\rm{Avg}_\rm{model}$
    SPIM-1 DT 7.75 14.72 5.32 3.56 6.88 9.54 0.18 15.28 10.11 0.14 0.13 6.69
    GBDT 8.39 5.62 9.13 8.95 8.28 6.26 8.80 6.58 5.19 8.77 8.60 7.69
    KNN 10.95 9.17 11.13 11.59 10.76 8.28 9.11 9.62 7.66 10.40 10.11 9.89
    LR 7.91 6.95 8.91 8.17 8.33 7.30 10.65 6.54 7.08 9.73 9.53 8.28
    NB 18.30 6.72 16.61 15.26 18.94 7.28 16.87 7.48 0.37 27.23 20.48 14.14
    RF 8.50 5.78 8.60 8.95 7.93 6.28 9.22 6.31 6.02 8.73 8.31 7.69
    XGBoost 7.04 4.75 6.80 6.74 6.35 5.56 6.11 5.81 4.16 5.77 5.94 5.91
    SPIM-8 DT 8.92 11.26 4.00 3.49 5.06 9.57 0.20 14.83 10.44 7.12 0.22 6.83
    GBDT 7.37 7.26 7.14 7.78 7.15 6.65 7.73 6.97 6.15 9.51 7.17 7.35
    KNN 11.32 9.77 11.13 10.53 10.26 9.51 10.67 9.69 7.98 11.78 9.04 10.15
    LR 8.30 6.68 9.78 9.42 9.72 6.65 12.01 6.70 6.69 5.59 12.16 8.52
    NB 15.11 6.36 14.60 13.03 19.90 7.84 15.20 ${\bf{-3.82}} $ 0.14 19.25 5.08 10.24
    RF 7.55 6.78 7.35 7.71 7.13 7.21 7.77 6.80 6.50 8.29 7.30 7.31
    XGBoost 6.62 5.76 6.07 6.54 6.37 5.30 5.65 6.16 4.68 7.87 5.61 6.06
    $\rm{Avg}_\rm{Feat}$ 9.57 7.68 9.04 8.69 9.50 7.37 8.58 7.50 5.94 10.01 7.83 /
    下载: 导出CSV

    表  9  Fabric数据集中, SGS_AVG类模型相对SGS_MAX类模型的Accuracy变化值$\Delta \rm{Accuracy}_\rm{F4}$ (性能衰减用${\bf{ -16.36}}$表示, 单位: %)

    Table  9  The accuracy variations of the SGS_AVG model compared to the SGS_MAX model in the Fabric dataset: $\Delta \text{Accurac}{{\text{y}}_{\text{F4}}}$ (The performance degradation indicators are marked in ${\bf{ -16.36}}$, unit: %)

    SPIF SGS模型 $\Delta \rm{Accuracy}_\rm{F4}=\rm{Accuracy}_\rm{SGS_AVG}\, -\, \rm{Accuracy}_\rm{SGS_MAX}$
    all S + G + L S + G + V S + L + V L + G + V S + G S + V S + L L + G L + V G + V $\rm{Avg}_\rm{model}$
    SPIF-1 DT 20.94 14.41 17.74 19.12 19.90 15.88 $ {\bf{ -4.58}}$ 6.56 -16.36 10.86 12.75 10.66
    GBDT 7.19 5.14 7.42 7.39 8.42 4.27 8.29 4.63 6.08 8.45 8.73 6.91
    KNN 19.31 29.86 18.99 24.29 11.06 30.25 24.03 40.25 9.28 14.89 13.06 21.39
    LR 10.31 7.46 11.06 9.32 10.98 6.93 9.36 6.17 11.93 10.54 12.32 9.67
    NB 15.94 25.04 16.39 18.64 13.11 17.85 16.31 6.55 24.37 10.31 6.00 15.50
    RF 9.48 8.41 9.64 10.58 11.65 7.39 11.22 8.49 10.66 13.03 11.89 10.22
    XGBoost 7.34 5.10 7.71 8.49 8.49 4.42 10.63 4.66 6.08 9.24 8.53 7.34
    SPIF-2 DT 8.88 8.21 5.61 4.98 4.03 4.19 2.25 4.07 4.82 2.17 2.84 4.73
    GBDT 7.98 8.01 7.62 7.90 5.61 5.69 5.25 5.97 5.76 4.74 5.33 6.35
    KNN 10.23 7.46 10.47 11.02 9.08 7.51 6.21 6.24 7.35 9.48 9.32 8.58
    LR 12.56 11.73 12.01 12.84 8.02 7.98 8.61 9.01 9.29 7.29 7.03 9.67
    NB 13.15 10.27 12.80 13.80 8.65 0.91 10.51 3.47 0.24 10.23 3.12 7.92
    RF 29.74 8.38 9.72 10.64 8.93 6.60 7.31 8.25 8.85 8.81 8.57 10.53
    XGBoost 9.08 8.93 8.61 8.85 6.59 6.44 6.12 6.99 6.67 5.41 5.89 7.23
    AvgFeat 13.01 11.32 11.13 11.99 9.61 9.02 8.68 8.67 6.79 8.96 8.24 /
    下载: 导出CSV

    表  10  MattrSet数据集中, 各基线最优值与本文模型的Accuracy比较(最优值如86.37等表示, 单位: %)

    Table  10  The best accuracy of each baseline in the MattrSet dataset is compared with the proposed model (The best value is marked as 86.37, etc., unit: %)

    Model Accuracy Model Accuracy
    1) SVM-S 50.83 2) GS-DT-SGL 46.20
    3) GBDT-L 61.28 4) GS-RF-LG 61.75
    5) Adaboost-L 61.54 6) GS-KNN-SGL 62.10
    7) XGBoost-L 62.93 8) GS-LR-SL 59.45
    9) VGG16 33.98 10) GS-NB-SG 49.61
    11) InceptionResNetV2 52.09 12) GS-GBDT-SGL 65.13
    13) Densenet169 59.77 14) GS-Adaboost-SGL 66.11
    15) MobileNets 33.98 16) GS-XGBoost-SGL[64] 67.67
    17) p*ncp-a-SGS-XGBoost-SGL 75.71 18) p*ncp-m-SGS-XGBoost-SGL 70.36
    19) cpp*n-a-SGS-NB-SV 86.37 20) cpp*n-m-SGS-RF-SGL 78.14
    21) p*pcn-a-SGS-XGBoost-SGL 80.00 22) p*pcn-m-SGS-XGBoost-SGL 74.22
    23) pcp*n-a-SGS-NB-SV 86.37 24) pcp*n-m-SGS-RF-SGL 78.54
    25) pnp*c-a-SGS-GBDT-SGL 80.05 26) pnp*c-m-SGS-XGBoost-LG 74.70
    27) npcp*-a-SGS-GBDT-SGL 79.71 28) npcp*-m-SGS-XGBoost-LG 74.24
    下载: 导出CSV

    表  11  Fabric数据集中, 各基线最优值与本文模型的Accuracy比较(最优值如97.55等表示, 单位: %)

    Table  11  The best accuracy of each baseline in the Fabric dataset is compared with the proposed model (The best value is marked as 97.55, etc., unit: %)

    Model Accuracy Model Accuracy
    1) SVM-S 77.92 2) GS-DT-SGL 65.64
    3) GBDT-S 79.66 4) GS-RF-SG 76.58
    5) AdaBoost-S 76.86 6) GS-KNN-LG 73.74
    7) XGBoost-S 82.03 8) GS-LR-SL 78.28
    9) VGG16 46.22 10) GS-NB-SG 57.42
    11) VGG-M[35] 79.60 12) GS-GBDT-SG 79.98
    13) Densenet169 46.22 14) GS-AdaBoost-SL 78.16
    15) MobileNet 46.22 16) GS-XGBoost-SG[64] 81.95
    17) sdtcvpfnw-m-SGS-XGBoost-SG 92.22 18) sdtcvpfnw-a-SGS-LR-SGL 97.55
    19) nsfvtwdpc-m-SGS-XGBoost-SG 83.37 20) nsfvtwdpc-a-SGS-LR-SL 91.71
    下载: 导出CSV

    表  12  MattrSet数据集中材质属性与实用属性之间的映射关系

    Table  12  The relationship between the material attributes and their utility attributes in the MattrSet dataset

    材质属性 二元映射关系 相对映射关系
    防水性 透气性 柔软性 水洗性 耐磨性 防水性 透气性 柔软性 水洗性 耐磨性
    Pu (皮革) 1 0 0 0 0 4 2 1 1 1
    Canvas (帆布) 0 1 0 1 1 1 4 2 4 4
    Polyester (涤纶) 1 0 1 1 0 3 1 4 2 3
    Nylon (尼龙) 0 0 1 1 0 2 3 3 3 2
    下载: 导出CSV

    表  13  Fabric数据集中材质属性与实用属性之间的映射关系

    Table  13  The relationship between the material attributes and their utility attributes in the Fabric dataset

    材质属性 二元映射关系 相对映射关系
    防水性 透气性 柔软性 耐磨性 防水性 透气性 柔软性 耐磨性
    Wool (羊毛) 0 1 1 0 1 8 8 1
    Denim (牛仔布) 0 0 0 1 8 1 1 8
    Viscose (粘胶纤维) 0 1 1 1 7 7 6 9
    Cotton (棉花) 0 1 1 0 4 5 5 3
    Silk (丝绸) 0 1 1 0 5 9 9 5
    Polyester (涤纶) 1 0 1 0 9 2 3 7
    Nylon (尼龙) 0 0 1 0 6 3 2 6
    Terrycloth (毛巾布) 0 1 1 1 3 4 4 4
    Fleece (摇粒绒) 0 1 1 0 2 6 7 2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-04
  • 录用日期:  2019-04-30
  • 刊出日期:  2020-10-29

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