结合轮廓及骨架序列编码的二维形状识别

卢勇强; 栗志扬; 陈祎楠; 刘朝斌; 黄一鸣

doi:10.13700/j.bh.1001-5965.2019.0376

结合轮廓及骨架序列编码的二维形状识别

doi: 10.13700/j.bh.1001-5965.2019.0376

大连海事大学信息科学技术学院, 大连 116026

基金项目:

国家自然科学基金 61300187

国家自然科学基金 61672379

辽宁省自然科学基金 2019-MS-028

详细信息

作者简介:
卢勇强  男, 硕士研究生。主要研究方向:计算机视觉、图像处理

栗志扬  男, 博士, 副教授, 硕士生导师。主要研究方向:计算机视觉、云计算与大数据

陈祎楠  男, 硕士研究生。主要研究方向:计算机视觉

刘朝斌  男, 博士, 教授, 博士生导师。主要研究方向:云计算与云安全

黄一鸣  男, 硕士研究生。主要研究方向:图像处理、组合优化

通讯作者:
栗志扬, E-mail: lizy0205@gmail.com

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2017-07-09
- 录用日期: 2019-08-12
- 网络出版日期: 2019-12-20

Two-dimensional shape recognition based on contour and skeleton sequence coding

School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

Funds:

National Natural Science Foundation of China 61300187

National Natural Science Foundation of China 61672379

Liaoning Provincial Natural Science Foundation of China 2019-MS-028

More Information

Corresponding author: LI Zhiyang, E-mail: lizy0205@gmail.com

摘要

摘要:
二维形状识别是物体识别中的一个基本问题，被广泛地应用于商标检索、指纹识别、物体定位、图像检索等多个领域。其中，基于生物信息学的二维形状识别是近期一个新的研究方向，基本思想是把二维形状的轮廓转化为生物信息序列，借助标准的生物信息序列分析工具来进行二维形状的匹配和识别。不过，利用轮廓进行信息序列编码存在编码冗余和编码准确性不高的问题，本文提出了一种新型的结合形状轮廓和骨架的序列编码方法。该方法利用骨架表示形状的细长分支，减少编码的冗余；并分别对轮廓和骨架进行不同类型的编码，具备编码简洁、后续匹配准确性高等优点。最后，本文在三个公开数据集上进行大量的形状识别实验，并与多种通用形状识别方法进行了比较。实验表明，本文方法在多个实验中均取得了较高的识别准确率，相比基本的形状特征描述方法，准确率提高了近5%。
- 形状匹配 /
- 轮廓特征 /
- 骨架特征 /
- 特征描述子 /
- 生物信息序列
Abstract:
Two-dimensional shape recognition is a fundamental problem in object recognition, which is widely used in trademark retrieval, fingerprint recognition, object location, image retrieval and other fields. Recently, two-dimensional shape recognition based on bioinformatics has become a new research direction, whose basic idea is to transform the contour of a planar shape into a biological information sequence. The two-dimensional shape matching and recognition are then achieved by the standard alignment tools of such biological information sequence. However, the classic coding method still suffers from the problems of code redundancy and low accuracy. In this paper, we present a new coding method based on both the shape contour and skeleton sequence. Firstly, skeletons are used to represent slender branches of the shape to reduce coding redundancy. Secondly, the contour and skeleton are coded in different ways to compact the code and improve the matching accuracy. Finally, extensive shape recognition experiments are conducted on three public datasets and the proposed method is compared with a variety of shape recognition methods. The experimental results demonstrate that the proposed method has achieved higher performance in several experiments, and the recognition accuracy rate is improved by nearly 5% compared with basic shape feature description methods.
- shape matching /
- contour features /
- skeleton features /
- feature descriptors /
- biological information sequences

HTML全文

图 1 二维形状匹配流程图

Figure 1. Flowchart of 2D shape matching

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图 2 形状的轮廓与骨架

Figure 2. Contour and skeleton of a shape

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图 3 形状细支处的轮廓与骨架

Figure 3. Contour and skeleton of branch of a shape

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图 4 轮廓与骨架的联合表示及默认的编码方向

Figure 4. A combined representation of contour and skeleton and default encoding direction

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图 5 编码构造规则

Figure 5. Encoding construction rule

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图 6 MPEG-7数据集

Figure 6. MPEG-7 dataset

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图 7 MPGE-7数据集可视化查询

Figure 7. Visual query of MPEG-7 dataset

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图 8 ETH-80数据集

Figure 8. ETH-80 dataset

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图 9 Swedish leaf数据集

Figure 9. Swedish leaf dataset

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图 10 MPEG-7数据集上不同编码策略分类准确率的比较

Figure 10. Comparison of classification accuracy rate on MPEG-7 dataset by different encoding strategies

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图 11 ETH-80数据集上不同编码策略分类准确率的比较

Figure 11. Comparison of classification accuracy rate on ETH-80 dataset by different encoding strategies

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表 1 MPEG-7数据集上牛眼比较法分类准确率对比

Table 1. Comparison of classification accuracy rate of bullseye method on MPEG-7 dataset

方法	准确率/%
IDSC+LP^[17]	91.61
IDSC+SSP^[18]	93.35
Layered graph^[19]	88.75
Aspect shape context^[3]	88.3
Shape tree^[1]	87.7
MDS+SC+DP^[2]	84.35
HPM^[20]	86.35
Symbolic representation^[21]	85.92
IDSC+DP^[2]	85.4
Bioinformatics classification^[5]	77.24
本文方法	88.64

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表 2 MPEG-7数据集上的分类准确率对比

Table 2. Comparison of classification accuracy rate on MPEG-7 dataset

方法	准确率/%
方法	留半法	留一法
Skeleton paths^[22]	86.7
Class segment set^[23]	90.9	97.93
Contour segments^[22]	91.1
ICS^[22]	96.5
Robust symbolic^[21]		98.57
Kernel-edit distance^[21]		98.93
BCF + SVM^[24]	97.16	98.93
Bioinformatics classification^[5]	95.85	98.1
本文方法	96.07	98.07

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表 3 ETH-80数据集上的分类准确率对比

Table 3. Comparison of classification accuracy rate on ETH-80 dataset

方法	准确率/%
Color histogram^[25]	64.86
PCA gray^[25]	82.99
PCA mask^[25]	83.41
SC+DP^[4]	86.40
IDSC+DP^[4]	88.11
IDSC+Morphological Strategy^[26]	88.04
Robust symbolic^[21]	90.28
Kernel-edit^[27]	91.33
BCF^[24]	91.49
Bioinformatics classification^[5]	91.33
本文方法	91.37

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表 4 Swedish leaf数据集上的分类准确率对比

Table 4. Comparison of classification accuracy rate on Swedish leaf dataset

方法	准确率/%
Fourier^[2]	89.60
SC+DP^[2]	88.12
IDSC+DP^[2]	94.13
IDSC+Morphological Strategy^[26]	94.80
Robust symbolic^[21]	95.47
Shape-tree^[20]	96.28
BCF^[24]	96.56
CNN^[28]	99.11
本文方法	94.67

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参考文献(29)

[1]	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. doi: 10.1109/34.993558
[2]	LING H, JACOBS W D.Shape classification using the inner-distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2):286-299. doi: 10.1109/TPAMI.2007.41
[3]	LING H, YANG X, LATECKI J L.Balancing deformability and discriminability for shape matching[C]//11th European Conference on Computer Vision.Berlin: Springer-Verlag, 2010, 6313(3): 411-424.
[4]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E.ImageNet classification with deep convolutional neural networks[C]//NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012: 1097-1105.
[5]	BICEGO M, LOVATO P.A bioinformatics approach to 2D shape classification[J]. Computer Vision and Image Understanding, 2016, 145:59-69. doi: 10.1016/j.cviu.2015.11.011
[6]	XU D, ALAMEDA-PINEDA X, SONG J, et al.Cross-paced representation learning with partial curricula for sketch-based image retrieval[J]. IEEE Transactions on Image Processing, 2018, 27(9):4410-4421. doi: 10.1109/TIP.2018.2837381
[7]	BISWAS S, AGGARWAL G, CHELLAPPA R.Efficient indexing for articulation invariant shape matching and retrieval[C]//2007 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2007: 1-8.
[8]	BLUM H.A transformation for extracting new descriptors of shape[M]//WATHEN DUNN W.Models for the perception of speech and visual form model.Cavnbridge: MIT Press, 1967: 362-380.
[9]	陈展展, 汤进, 罗斌, 等.基于最优子序列双射的骨架树匹配[J].计算机工程与应用, 2011, 41(7):162-165. doi: 10.3778/j.issn.1002-8331.2011.07.047 CHEN Z Z, TANG J, LUO B, et al.Skeleton tree matching based on optimal subsequence bijection[J]. Computer Engineering and Applications, 2011, 41(7):162-165(in Chinese). doi: 10.3778/j.issn.1002-8331.2011.07.047
[10]	BAI X, LATECKI J L.Path similarity skeleton graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(7):1282-1292. doi: 10.1109/TPAMI.2007.70769
[11]	NEEDLEMAN S, WUNSCH C.A general method applicable to the search for similarities in the amino acid sequence of two proteins[J]. Journal of Molecular Biology, 1970, 48(31):443-453. doi: 10.1016-0022-2836(70)90057-4/
[12]	SMITH T, WATERMAN S M M.Identification of common molecular subsequences[J]. Journal of Molecular Biology, 1981, 147(1):195-197. doi: 10.1016/0022-2836(81)90087-5
[13]	ALTSCHUL S, GISH W, MILLER W, et al.Basic local alignment search tool[J]. Journal of Molecular Biology, 1990, 2145(3):403-410. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0231404456/
[14]	LARKIN M, BLACKSHIELDS G, BROWN N, et al.Clustal w and clustal x version 2.0[J]. Bioinformatics, 2007, 23(21):2947-2948. doi: 10.1093/bioinformatics/btm404
[15]	MARIE R, LABBANI-IGBIDA O, MOUADDIB M E.The delta medial axis:A fast and robust algorithm for filtered skeleton extraction[J]. Pattern Recognition, 2016, 56:26-39. doi: 10.1016/j.patcog.2016.02.011
[16]	GOS'CIEWSKA K, FREJLICHOWSKI D.Silhouette-based action recognition using simple shape descriptors[C]//Lecture Notes in Computer Science.Berlin: Springer-Verlag, 2018, 11114: 413-424.
[17]	BAI X, YANG X, LATECKI J L, et al.Learning context-sensitive shape similarity by graph transduction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 32(5):861-874. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b7ad1715655a7dd6a19478938738e5fb
[18]	WANG J, LI Y, BAI X, et al.Learning context-sensitive similarity by shortest path propagation[J]. Pattern Recognition, 2011, 44(10):2367-2374. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=2bc10153182479494c7603fef9d53503
[19]	LIN L, ZENG K, LIU X, et al.Layered graph matching by composite cluster sampling with collaborative and competitive interactions[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2009: 1351-1358.
[20]	MCNEILL G, VIJAYAKUMAR S.Hierarchical procrustes matching for shape retrieval[C]//2006 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2006: 885-894.
[21]	DALIRI R M, TORRE V.Robust symbolic representation for shape recognition and retrieval[J]. Pattern Recognition, 2008, 41(5):1782-1798. doi: 10.1016/j.patcog.2007.10.020
[22]	BAI X, LIU W, TU Z.Integrating contour and skeleton for shape classification[C]//2009 IEEE 12th International Conference on Computer Vision Workshops.Piscataway, NJ: IEEE Press, 2009: 360-367.
[23]	SUN B K, SUPER J B.Classification of contour shapes using class segment sets[C]//2005 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2005: 727-733.
[24]	WANG X, FENG B, BAI X, et al.Bag of contour fragments for robust shape classification[J]. Pattern Recognition, 2014, 47(5):2116-2125. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0c6be3652bf5467b4efb55bfe21bb755
[25]	LEIBE B, SCHIELE B.Analyzing appearance and contour based methods for object categorization[C]//2003 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2003: 409-415.
[26]	HU X R, JIA W, ZHAO Y, et al.Perceptually motivated morphological strategies for shape retrieval[J]. Pattern Recognition, 2012, 45(9):3222-3230. doi: 10.1016/j.patcog.2012.02.020
[27]	DALIRI R M, TORRE V.Shape recognition based on Kernel-edit distance[J]. Computer Vision and Image Understanding, 2010, 114(10):1097-1103. doi: 10.1016/j.cviu.2010.07.002
[28]	ATABAY A H.A convolutional neural network with a new architecture applied on leaf classification[J]. ⅡOAB Journal, 2016, 7(5):326-331.
[29]	GAO L, SONG J, NIE F, et al.Optimal graph learning with partial tags and multiple features for image and video annotation[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2015: 4371-4379.