Feature description of exact height function used in fast shape retrieval

Guo-dong SUN; Yang ZHANG; Ping LI; Shu-zheng MEI; Da-xing ZHAO

doi:10.3788/OPE.20172501.0224

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Feature description of exact height function used in fast shape retrieval

Information Sciences | 更新时间：2020-07-07

- Feature description of exact height function used in fast shape retrieval
- Optics and Precision Engineering Vol. 25, Issue 1, Pages: 224-235(2017)
- 作者机构：
  
  湖北工业大学机械工程学院, 湖北武汉 430068
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172501.0224
  CLC： TP391
- Received：04 August 2016，
  
  Accepted：01 October 2016，
  
  Published：25 January 2017
- 稿件说明：
移动端阅览
Guo-dong SUN, Yang ZHANG, Ping LI, et al. Feature description of exact height function used in fast shape retrieval[J]. Optics and precision engineering, 2017, 25(1): 224-235.
DOI：

Guo-dong SUN, Yang ZHANG, Ping LI, et al. Feature description of exact height function used in fast shape retrieval[J]. Optics and precision engineering, 2017, 25(1): 224-235. DOI： 10.3788/OPE.20172501.0224.

摘要

在形状匹配过程中为了提升高度函数描述子的检索精度和对边界噪声与局部变形的鲁棒性，本文提出了一种精确型高度函数特征描述算法。首先提取目标形状外轮廓，构造轮廓采样点的精确型高度函数描述子并进行特征降维，接着利用优化后的并行动态规划进行形状匹配，最后引入形状复杂度分析提升匹配效果。基于点的几何特征显著性，提出形状精度理论，进一步分析局部形变与边缘噪声对形状特征描述的影响。在MPEG-7数据库、Swedish Leaf数据库、Tools数据库和ETH-80大型3D数据库上进行匹配实验以及在Kimia99数据库上进行抗噪实验，实验结果表明：本文提出的算法效率高，匹配时间仅为高度函数描述子的12.5%，在MPEG-7和ETH-80上的检索率最高分别为90.38%和90.07%；在Swedish Leaf和Tools上，检索精度最高分别为95.07%和94.86%，检索性能和鲁棒性均优于高度函数和其他重要算法；在添加噪声的Kimia 99上，该算法的抗噪性能优于高度函数描述子，即使在噪声水平为2.0的情况下，依旧能保持91.92%的检索率。本文提出的算法检索精度高，效率高，鲁棒性好，抗噪性强，具有较好的可扩展性，能有效地应用于形状检索领域。

Abstract

In order to improve the discrimination ability and robustness of contour noise and deformation of Height Functions (HF) descriptor in the process of shape retrieval

a feature description algorithm of exact height functions is proposed in shape retrieval. Firstly

contour outside the target shape is extracted

and then exact height functions type descriptors of sampling points are constructed for dimensionality reduction. And then

the optimized parallel dynamic programming algorithm is employed in matching stage. Finally

shape complexity analysis is used to improve matching effect. Based on point geometric feature saliency

the shape precision theory is proposed to further analyze the influence of the local deformation and the edge noise on shape feature description. The matching experiment has been conducted on the database of MPEG-7

Swedish Leaf

Tools

ETH-80 and noise experiment has been conducted on Kimia99 database. Experimental results indicate that the proposed algorithm in this paper is highly efficient and the matching time of it is only 12.5% of the original HF descriptor. The highest retrieval ratio can reach 90.38% on MPEG-7

90.07% on ETH-80

95.07% on Swedish Leaf and 94.86% on Tools respectively and retrieval performance and robustness are better than HF and other important algorithms; on Kimia99 with adding noise

the anti-noise performance of the proposed algorithm is superior to the original HF descriptor

and even in the case of noise level of 2.0

the algorithm can still keep a retrieval rate of 91.92%. The proposed algorithm

with high accuracy

high efficiency

great robustness and noise immunity and good scalability

can be effectively applied to shape retrieval field.

关键词

Keywords

references

胡大盟,黄伟国,杨剑宇,等. 改进离散曲线演化的形状匹配算法[J]. 计算机辅助设计与图形学学报,2015,27(10):1865-1873.

HU D M, HUANG W G, YANG J Y, et al.. Improved shape matching algorithm based on discrete curve evolution[J]. Journal of Computer Aided Design and Computer Graphics, 2015, 27(10):1865-1873.(in Chinese)

李平,魏仲慧,何昕,等. 采用多形状特征融合的多视点目标识别[J]. 光学精密工程,2014,22(12):3368-3376.

LI P, WEI ZH H, HE X, et al.. Object recognition based on shape feature fusion under multi-views[J]. Opt. Precision Eng., 2014, 22(12):3368-3376.(in Chinese)

崔雄文,吴钦章,蒋平,等. 子空间模型下的仿射不变目标跟踪[J]. 红外与激光工程,2015,44(2):769-774.

CUI X W, WU Q ZH, JIANG P, et al.. Affine-invariant target tracking based on subspace representation[J]. Infrared and Laser Engineering, 2015, 44(2):769-774.(in Chinese)

杜杰,吴谨,朱磊. 基于区域特征融合的RGBD显著目标检测[J]. 液晶与显示,2016,31(1):117-123.

DU J, WU J, ZHU L. RGBD salient object detection based on regional feature integration[J]. Chinese Journal of Liquid Crystals and Displays, 2016, 31(1):117-123.(in Chinese)

李志军,刘松林,牛照东,等. 基于梯度相位和显著性约束的Hausdorff距离模板匹配方法[J]. 红外与激光工程,2015,44(2):775-780.

LI ZH J, LIU S L, NIU ZH D, et al.. Hausdorff distance template matching method based on gradient phase and significance constraints[J]. Infrared and Laser Engineering, 2015, 44(2):775-780.(in Chinese)

周瑜,刘俊涛,白翔. 形状匹配方法研究与展望[J]. 自动化学报,2012,38(6):889-910.

ZHOU Y, LIU J T, BAI X. Research and perspective on shape matching[J]. Acta Automatica Sinica, 2012, 38(6):889-910.(in Chinese)

BELONGIE S, MALIK J, PUZICHA J. Shape matching and object recognition using shape contexts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4):509-522.

LING H, JACOBS D W. Shape classification using the inner-distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2):286-299.

吴晓雨,何彦,杨磊,等. 基于改进形状上下文特征的二值图像检索[J]. 光学精密工程,2015,23(1):302-309.

WU X Y, HE Y, YANG L, et al.. Binary image retrieval based on improved shape context algorithm[J]. Opt. Precision Eng., 2015, 23(1):302-309.(in Chinese)

杨亚飞,郑丹晨,韩敏. 一种基于多尺度轮廓点空间关系特征的形状匹配方法[J]. 自动化学报,2015,41(8):1405-1411.

YANG Y F, ZHENG D CH, HANG M.A shape matching method using spatial features of multi-scaled contours[J]. Acta Automatica Sinica, 2015, 41(8):1405-1411.(in Chinese)

黄伟国,胡大盟,杨剑宇,等. 用于遮挡形状匹配的弦角特征描述[J]. 光学精密工程,2015,23(6):1758-1767.

HUANG W G, HU D M, YANG J Y, et al.. Chord angle representation for shape matching under occlusion[J]. Opt. Precision Eng., 2015, 23(6):1758-1767.(in Chinese)

WANG J W, BAI X, YOU X, et al.. Shape matching and classification using height functions[J]. Pattern Recognition Letters, 2012, 33(2):134-143.

WANG B, BROWN D, GAO Y, et al.. MARCH:Multiscale arch-height description for mobile retrieval of leaf images[J]. Information Sciences, 2015, 302:132-148.

王斌. 一种基于多尺度拱高形状描述的图像检索方法[J]. 电子学报,2013,41(9):1821-1825.

WANG B. Image retrieval using multi-scale arch height shape description[J]. Acta Electronica Sinica, 2013, 41(9):1821-1825.(in Chinese)

NANNI L, BRAHNAM S, LUMINI A. Local phase quantization descriptor for improving shape retrieval/classification[J]. Pattern Recognition Letters, 2012, 33(16):2254-2260.

SHEKAR B H, PILAR B. Discrete cosine transformation and height functions based shape representation and classification[J]. Procedia Computer Science, 2015, 58:714-722.

SHEKAR B H, PILAR B. An ensemble of morphology based pattern spectrum and height functions for shape representation and classification[C]. Proceedings of International Conference on Advances in Computing, Communications and Informatics, 2014:2264-2272.

王林波,王延杰,邸男,等. 基于几何特征的圆形标志点亚像素中心定位[J]. 液晶与显示,2014,29(6):1003-1009.

WANG L B, WANG Y J, DI N, et al.. Subpixel location of circle target center based on geometric features[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(6):1003-1009.(in Chinese)

TSANG P W M, YUEN P C, LAM F K. Classification of partially occluded objects using 3-point matching and distance transformation[J]. Pattern Recognition, 1994, 27(1):27-40.

JIANG S, WANG C, XU X. Fast algorithm for multi-source image registration based on geometric feature of corners[C].Proceedings of International Conference on Advanced Intelligent Computing Theories and Applications:Intelligent Computing, 2010:438-446.

LATECKI L J, LAKÄMPER R, ECKHARDT U. Shape descriptors for non-rigid shapes with a single closed contour[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000:424-429.

SÖDERKVIST O. Computer vision classification of leaves from Swedish trees[D]. Sweden:Link ping University, 2001.

BRONSTEIN A M, BRONSTEIN M M, BRUCKSTEIN A M,et al.. Analysis of two dimensional non-rigid shapes[J]. International Journal of Computer Vision, 2008, 78(1):67-88.

LEIBE B, SCHIELE B. Analyzing appearance and contour based methods for object categorization[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003:409-415.

SEBASTIAN T B, KLEIN P N, KIMIA B B. Recognition of shapes by editing their shock graphs[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2004, 26(5):550-571.

DALIRI M R, TORRE V. Robust symbolic representation for shape recognition and retrieval[J]. Pattern Recognition, 2008, 41(5):1782-1798.

ZHI Y L, WEN Y Q, JUN J C, et al.. ECDS:An effective shape signature using electrical charge distribution on the shape[J]. Pattern Recognition, 2015, 48(2):402-410.

BAI X, YANG X, LATECKI L J, et al.. Learning context-sensitive shape similarity by graph transduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5):861-874.

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