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一种基于改进地貌形状上下文的形状匹配方法

刘望舒 郑丹晨 韩敏

刘望舒, 郑丹晨, 韩敏. 一种基于改进地貌形状上下文的形状匹配方法. 自动化学报, 2017, 43(10): 1749-1758. doi: 10.16383/j.aas.2017.c160302
引用本文: 刘望舒, 郑丹晨, 韩敏. 一种基于改进地貌形状上下文的形状匹配方法. 自动化学报, 2017, 43(10): 1749-1758. doi: 10.16383/j.aas.2017.c160302
LIU Wang-Shu, ZHENG Dan-Chen, HAN Min. Shape Matching Method Based on Improved Aspect Shape Context. ACTA AUTOMATICA SINICA, 2017, 43(10): 1749-1758. doi: 10.16383/j.aas.2017.c160302
Citation: LIU Wang-Shu, ZHENG Dan-Chen, HAN Min. Shape Matching Method Based on Improved Aspect Shape Context. ACTA AUTOMATICA SINICA, 2017, 43(10): 1749-1758. doi: 10.16383/j.aas.2017.c160302

一种基于改进地貌形状上下文的形状匹配方法

doi: 10.16383/j.aas.2017.c160302
基金项目: 

国家自然科学基金 61374154

中央高校基本科研业务费专项资金 DUT16RC(4)18

详细信息
    作者简介:

    刘望舒 大连理工大学电子信息与电气工程学部硕士研究生.主要研究方向为模式识别.E-mail:liuwangshu@mail.dlut.edu.cn

    郑丹晨  大连理工大学电子信息与电气工程学部讲师.主要研究方向为计算机视觉和模式识别.E-mail:dcjeong@dlut.edu.cn

    通讯作者:

    韩敏 大连理工大学电子信息与电气工程学部教授.主要研究方向为模式识别, 复杂系统建模与分析及时间序列预测.本文通信作者.E-mail:minhan@dlut.edu.cn

Shape Matching Method Based on Improved Aspect Shape Context

Funds: 

National Natural Science Foundation of Chin 61374154

Fundamental Research Funds for the Central Universities DUT16RC(4)18

More Information
    Author Bio:

    Master student at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her main research interest is pattern recognition

    Lecturer at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. He received his Ph. D. degree from Dalian University of Technology in 2014. His research interest covers computer vision and pattern recognition

    Corresponding author: HAN Min  Professor at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers pattern recognition, modeling and analysis of complex system, and time series prediction. Corresponding author of this paper.E-mail:minhan@dlut.edu.cn
  • 摘要: 在基于地貌形状上下文的形状匹配方法中,计算地貌空间测地距离消耗时间较高,对应形状特征提取过程的效率较低.针对这一问题,本文提出了一种基于地貌模糊形状上下文的快速形状匹配方法.在形状特征提取过程中,通过引入最短路径算法对轮廓采样点间的测地距离进行快速计算.在此基础上结合对数极坐标模糊直方图构造地貌模糊形状上下文,其能够更好地描述轮廓点分布情况进而有效提升形状描述符的表达能力.考虑到轮廓点集顺序已知,进一步引入动态规划分析不同地貌空间下形状片段间的对应关系,以获取准确的形状匹配结果.通过对不同的数据集进行实验仿真分析,验证了本文方法能够有效地提升运算效率并取得较好形状检索精度.
    1)  本文责任编委 胡清华
  • 图  1  以不同距离度量分析相似形状对应采样点关系的示例

    Fig.  1  An example of analyzing the distances between corresponding points from two similar shapes with different measures

    图  2  一个二值图像对应生成的地貌空间示例

    Fig.  2  An example of the aspect space obtained from a binary image

    图  3  地貌空间中测地线路径示例

    Fig.  3  An example of geodesic in the aspect space

    图  4  分析不同方法计算测地距离效率

    Fig.  4  Efficiency comparison of computing geodesic distances by using different methods

    图  5  Kimia-99数据集中形状样本示例

    Fig.  5  Examples of shapes in Kimia-99 database

    图  6  参数τ变化对应的PR曲线

    Fig.  6  Precision-recall curves for different τ

    图  7  Kimia-216数据集中形状样本示例

    Fig.  7  Examples of shapes in Kimia-216 database

    图  8  Tari-1000形状样本示例

    Fig.  8  Examples of shapes in Tari-1000 database

    表  1  Kimia-99数据形状特征提取时间比较(s)

    Table  1  Comparison of the time used for shape feature extraction on Kimia-99 database (s)

    方法 特征提取时间(s)
    Fast marching[11] 208.56
    本文方法 11.77
    下载: 导出CSV

    表  2  Kimia-99数据在不同方法下检索结果比较

    Table  2  Comparison of retrieval rates for different algorithms tested on Kimia-99 database

    方法 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 全部
    SC[6] 97 91 88 85 84 77 75 66 56 37 756
    Hierarchical parts[16] 99 99 98 98 98 97 96 94 93 82 954
    IDSC + DP[10] 99 99 99 98 98 97 97 98 94 79 958
    MDS + SC + DP[10] 99 98 98 98 97 99 97 96 97 85 964
    GM + SC[17] 99 99 99 99 99 99 99 97 93 86 969
    Height functions[18] 99 99 99 99 98 99 99 96 95 88 971
    FSC + SCS[13] 99 99 99 99 98 99 98 95 94 91 972
    Symbolic representation[19] 99 99 99 98 99 98 98 95 96 94 975
    AFSC 99 99 99 99 99 99 99 99 98 92 982
    下载: 导出CSV

    表  3  Kimia-216数据在不同方法下检索结果比较

    Table  3  Comparison of retrieval rates for different algorithms tested on Kimia-216 database

    方法 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 全部
    Generative model[20] 216 216 214 212 212 207 203 190 179 175 2024
    Shock edit[21] 216 216 216 215 210 210 207 204 200 187 2081
    SC + DP[10] 216 216 215 210 210 209 208 204 200 191 2079
    FSC + SCS[13] 216 216 215 214 213 214 210 201 193 188 2080
    Path similarity[22] 216 216 215 216 213 210 210 207 205 191 2099
    IDSC + DP[10] 216 216 215 211 211 210 211 207 203 198 2098
    AFSC 216 216 216 214 214 215 209 207 209 199 2115
    下载: 导出CSV

    表  4  Tari-1000数据在不同方法下的结果比较

    Table  4  Comparison of results for different algorithms tested on Tari-1000 database

    方法 Bull's eye score
    Tree edit + Context[23] 86.42
    Similar and discriminative parts[24] 91.37
    COP[25] 92.18
    SC + DP[4] 94.17
    Group-wised[26] 94.58
    IDSC + DP[10] 95.33
    ASC + DP[11] 95.44
    AFSC 97.21
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-04-01
  • 录用日期:  2016-08-23
  • 刊出日期:  2017-10-20

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