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Shape retrieval using triangle-area representation and dynamic space warping. (English) Zbl 1120.68046

Summary: In this paper, we present a shape retrieval method using triangle-area representation for nonrigid shapes with closed contours. The representation utilizes the areas of the triangles formed by the boundary points to measure the convexity/concavity of each point at different scales (or triangle side lengths). This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, and scaling, and robust against noise and moderate amounts of occlusion. In the matching stage, a dynamic space warping algorithm is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Then, a distance is derived based on the optimal correspondence. The performance of our method is demonstrated using four standard tests on two well-known shape databases. The results show the superiority of our method over other recent methods in the literature.

MSC:

68P20 Information storage and retrieval of data
68T10 Pattern recognition, speech recognition
68P15 Database theory
90C39 Dynamic programming
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References:

[1] Lambert, S.; de Leau, E.; Vuurpijl, L., Using pen-based outlines for object-based annotation and image-based queries, (), 585-592
[2] J.M. Martinez, Mpeg-7 overview (version 9), Technical Report ISO/IEC JTC1/SC29/WG11N5525, ISO/IEC JTC1/SC29/WG11, International Organisation for Standardisation, Coding of Moving Pictures and Audio, March 2003.
[3] El Rube, I.; Alajlan, N.; Kamel, M.; Ahmed, M.; Freeman, G., Robust multiscale triangle-area representation for 2d shapes, (), 545-548
[4] El Rube, I.; Alajlan, N.; Kamel, M.; Ahmed, M.; Freeman, G., Efficient multiscale shape-based representation and retrieval, (), 415-422
[5] Roh, K.; Kweon, I., 2-d object recognition using invariant contour descriptor and projective refinement, Pattern recognition, 31, 4, 441-445, (1998)
[6] Ip, H.H.S.; Shen, D.G., An affine-invariant active contour model (ai-snake) for model-based segmentation, Image vision comput., 16, 2, 135-146, (1998)
[7] Shen, D.G.; Wong, W.; Ip, H.H.S., Affine invariant image retrieval by correspondence matching of shapes, Image vision comput., 17, 7, 489-499, (1999)
[8] Shen, D.G.; Ip, H.H.S.; Teoh, E.K., Affine invariant detection of perceptually parallel 3d planar curves, Pattern recognition, 33, 11, 1909-1918, (2000)
[9] I. El Rube, M. Ahmed, M. Kamel, Coarse-to-fine multiscale affine invariant shape matching and classification, in: 17th International Conference on Pattern Recognition (ICPR), vol. 2, Cambridge, UK, 2004, pp. 163-166.
[10] El Rube, I.; Ahmed, M.; Kamel, M., Affine invariant multiscale wavelet-based shape matching algorithm, (), 217-224
[11] The MPEG Home Page, \(\langle\)http://www.chiariglione.org/mpeg⟩.
[12] Loncaric, S., A survey of shape analysis techniques, Pattern recognition, 31, 8, 983-1001, (1998)
[13] Zhang, D.; Lu, G., Review of shape representation and description techniques, Pattern recognition, 37, 1, 1-19, (2004)
[14] Bartolini, I.; Ciaccia, P.; Patella, M., Warp: accurate retrieval of shapes using phase of Fourier descriptors and time warping distance, IEEE trans. pattern anal. Mach. intell., 27, 1, 142-147, (2005)
[15] Abbasi, S.; Mokhtarian, F.; Kittler, J., Curvature scale space image in shape similarity retrieval, Multimedia syst., 7, 6, 467-476, (1999)
[16] H. Ling, D. Jacobs, Using the inner distance for classification of articulated shapes, in: IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, San Diego, CA, USA, 20-26 June 2005, pp. 719-726.
[17] Belongie, S.; Malik, J.; Puzicha, J., Shape matching and object recognition using shape contexts, IEEE trans. pattern anal. Mach. intell., 24, 24, 509-522, (2002)
[18] Adamek, T.; O’Connor, N.E., A multiscale representation method for nonrigid shapes with a single closed contour, IEEE trans. circuits syst. video techol., 14, 5, 742-753, (2004)
[19] Petrakis, E.G.M.; Diplaros, A.; Milios, E., Matching and retrieval of distorted and occluded shapes using dynamic programming, IEEE trans. pattern anal. Mach. intell., 24, 11, 1501-1516, (2002)
[20] Sebastian, T.; Klein, P.; Kimia, B., On aligning curves, IEEE trans. pattern anal. Mach. intell., 25, 1, 116-124, (2003)
[21] Arica, N.; Vural, F., Bas: a perceptual shape descriptor based on the beam angle statistics, Pattern recognition lett., 24, 9-10, 1627-1639, (2003) · Zbl 1048.68072
[22] Latecki, L.J.; Lakamper, R., Shape similarity measure based on correspondence of visual parts, IEEE trans. pattern anal. Mach. intell., 22, 10, 1185-1190, (2000)
[23] Latecki, L.J.; Lakamper, R.; Wolter, D., Optimal partial shape similarity, Image vision comput., 23, 227-236, (2005)
[24] Itakura, F., Minimum prediction residual principle applied to speech recognition, IEEE trans. acoust. speech signal process. ASSP, 23, 52-72, (1975)
[25] Sakoe, H.; Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, IEEE trans. acoust. speech signal process., 26, 43-49, (1978) · Zbl 0371.68035
[26] Deller, J.; Hansen, J.; Proakis, J., Discrete-time processing of speech signals, (1999), Wiley-IEEE Press New York
[27] Wang, K.; Gasser, T., Alignment of curves by dynamic time warping, Ann. statist., 25, 3, 1251-1276, (1997) · Zbl 0898.62051
[28] Jain, A.K.; Vailaya, A., Shape-based retrieval: a case study with trademark image databases, Pattern recognition, 31, 9, 1369-1390, (1998)
[29] L.J. Latecki, R. Lakamper, U. Eckhardt, Shape descriptors for non-rigid shapes with a single closed contour, In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2000, pp. 424-429.
[30] Sebastian, T.; Klein, P.; Kimia, B., Recognition of shapes by editing their shock graphs, IEEE trans. pattern anal. Mach. intell., 26, 5, 550-571, (2004)
[31] Mokhtarian, F.; Bober, M., Curvature scale space representation: theory, applications, and MPEG-7 standardization, (2003), Kluwer Academic Publishers · Zbl 1022.68120
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