Abstract
To improve computational efficiency and solve the problem of low accuracy caused by geometric transformations and nonlinear deformations in the shape-based object recognition, a novel contour signature is proposed. This signature includes five types of invariants in different scales to obtain representative local and semi-global shape features. Then the Dynamic Programming algorithm is applied to shape matching to find the best correspondence between two shape contours. The experimental results validate that our methods is robust to rotation, scaling, occlusion, intra-class variations and articulated variations. Moreover, the superior shape matching and retrieval accuracy on benchmark datasets verifies the effectiveness of our method.
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References
Drira, H., Ben Amor, B., Srivastava, A., Daoudi, M., Slama, R.: 3D face recognition under expressions, occlusions, and pose variations. In: 2013 IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35(9), pp. 2270–2283 (2013)
Wang, J., Li, Y., Bai, X., et al.: Learning context-sensitive similarity by shortest path propagation. Pattern Recogn. 41, 2367–2374 (2011)
Wolter, D., Latecki, L.J.: Shape matching for robot mapping. In: Pacific Rim International Conference on Artificial Intelligence (2004)
Ling, H., Jacobs, D.W.: Shape classification using the inner-distance. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 286–299 (2007)
Alajlan, N., Rube, I.E., Kamel, M.S., Freeman, G.: Shape retrieval using triangle area representation and dynamic space warping. Pattern Recogn. 40, 1911–1920 (2007)
Manay, S., Cremers, D., Hong, B.-W., Yezzi, A.J., Soatto, S.: Integral invariants for shape matching. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1602–1618 (2006)
Bai, X., Yang, X., Latecki, L.J., Liu, W., Tu, Z.: Learning context-sensitive shape similarity by graph transduction. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 861–874 (2010)
Bai, X., Rao, C., Wang, X.: Shape vocabulary: a robust and efficient shape representation for shape matching. IEEE Trans. Image Process. 23(9), 3935–3949 (2014)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2001)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)
Alajlan, N., Kamel, M.S., Freeman, G.H.: Geometry-based image retrieval in binary image databases. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 1003–1013 (2008)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: On aligning curves. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 116–125 (2003)
Klassen, E., Srivastava, A., Mio, W., Joshi, S.H.: Analysis of planar shapes using geodesic paths on shape spaces. IEEE Trans. Pattern Anal. Mach. Intell. 26(3), 372–383 (2004)
Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and robust retrieval by shape content through curvature scale space. Ser. Softw. Eng. Knowl. Eng. 8, 51–58 (1997)
Attalla, E., Siy, P.: Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching. Pattern Recogn. 38(12), 2229–2241 (2005)
Yang, J., Wang, H., Yuan, J., Li, Y.F., Liu, J.: Invariant multi-scale descriptor for shape representation, matching and retrieval. Comput. Vis. Image Underst. (CVIU) 145, 43–58 (2016)
Yang, J., Xu, H.: Metric learning based object recognition and retrieval. Neurocomputing 190, 70–81 (2016)
Yang, J., Yuan, J., Li, Y.F.: Parsing 3D motion trajectory for gesture recognition. J. Vis. Commun. Image Representation (JVCI) 38, 627–640 (2016)
Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape descriptors for non-rigid shapes with a single closed contour. In: IEEE Conference on Computer Vision and Pattern Recognition, Proceedings. vol. 1, pp. 424–429 (2000)
Xu, C., Liu, J., Tag, X.: 2D Shape matching by contour flexibility. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 180–186 (2009)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 550–571 (2004)
Shu, X., Wu, X.: A novel contour descriptor for 2D shape matching and its application to image retrieval. Image Vis. Comput. 29(4), 286–294 (2011)
Tu, Z., Yuille, A.: Shape matching and recognition-using generative models and informative features. In: Proceedings of European Conference Computer Vision (ECCV), Prague, Czech Republic, pp. 195–209 (2004)
Acknowledgements
This work was funded by research grants from the National Natural Science Foundation of China (NSFC No. 61305020 and No. 61273286), and the Natural Science Foundation of Jiangsu province, China (Grant No. BK20130316).
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Xu, H., Yang, J., Shao, Z., Tang, Y., Li, Y. (2016). Contour Based Shape Matching for Object Recognition. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9834. Springer, Cham. https://doi.org/10.1007/978-3-319-43506-0_25
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DOI: https://doi.org/10.1007/978-3-319-43506-0_25
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