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

LU Yongqiang; LI Zhiyang; CHEN Yinan; LIU Zhaobin; HUANG Yiming

doi:10.13700/j.bh.1001-5965.2019.0376

Volume 45 Issue 12

Dec. 2019

Turn off MathJax

Article Contents

Abstract

References

Journal of Beijing University of Aeronautics and Astronautics > 2019 > 45(12): 2523-2532.

Wang Bin, Tang Xiaoqing. Evaluating failure risk for product design scheme based on knowledge model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(7): 763-768. (in Chinese)

Citation:

LU Yongqiang, LI Zhiyang, CHEN Yinan, et al. Two-dimensional shape recognition based on contour and skeleton sequence coding[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2523-2532. doi: 10.13700/j.bh.1001-5965.2019.0376(in Chinese)

Citation:

PDF( 3089 KB)

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

doi: 10.13700/j.bh.1001-5965.2019.0376

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
Received Date: 09 Jul 2017
Accepted Date: 12 Aug 2019
Publish Date: 20 Dec 2019

Abstract

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

FullText(HTML)

References(29)

References

[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.
[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.
[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.
[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.
[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.