A New Shape Retrieval Method Using the Group Delay of The Fourier Descriptors

Document Type : Research Paper

Authors

semnan university

Abstract

In this paper, we have introduced a new way to analyze binary shapes using a new Fourier based descriptor, which is the smoothed derivative of the phase of the Fourier descriptors. It is extracted from the complex boundary of the shape, and it is called the smoothed group delay (SGD). The usage of SGD on the Fourier phase descriptors, allows a compact representation of the shape boundaries which is robust to noise and can be easily made independent of translation, scaling, rotation and changes in the starting point used to describe each boundary. In this study, we have used phase normalization for invariance against rotation and translation and scaling and changes in the starting point. Then the cross-correlation of the SGDs of shapes is used as a similarity measure. In this paper, precision-recall curve and the average normalized modified retrieval rank (ANMRR) are used to evaluate the retrieval performance of the proposed method. Finally, this method is applied on the standard shape database MPEG-7 CE-Shape-1 and the experimental results show the superiority of the proposed method compared to the other methods.

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References

[1] T. Pavlidis, Survey of shape analysis methods, Comput. Graphics Image Process.
[2] J. Ahmad, Z. Jan, Z. Din and S. M. Khan, A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition, World Applied Sciences Journal 31 (6), pp. 1207-1213, 2014.
[3] D. Zhang, Image retrieval based on shape. Ph.D. Thesis, Monash University, 2002.
[4] D. Zhang and G. Lu, Improving retrieval performance of Zernike moment descriptor on affined shapes. GSCIT, 2002.
[5] R. B. Yadav, N. K. Nishchal, A. K. Gupta, V. K. R. Raj , Retrieval and classification of objects using generic Fourier,Legendre moment, and wavelet Zernike moment descriptors and recognition using joint transform correlator journal of Optics & Laser Technology 40, pp. 517–527, 2008.
[6] T.V. Hoang, S. Tabbone, A geometric invariant shape descriptor based on theradon, fourier, and mellin transforms, 20th International Conference on Pattern Recognition, pp. 2085-2088, 2010.
[7] A. Sajjanhar, G. Lu, D. Zhang and W. Zhou, Corners-based composite descriptor for shapes. Proceeding of the International Conference on Computer and Information Science, May 27-30, IEEE Xplore Press, Sanya, China, pp. 714-718, 2008.
[8] D.S. Zhang, G. Lu, Study and evaluation of different Fourier methods for image retrieval, Image and Vision Computing 23, pp. 33–49, 2005.
[9] P. L. E. Ekombo, N. Ennahnahi, M. Oumsis, M. Meknassi, Application of affine invariant Fourier descriptor to shape-based image retrieval, IJCSNS International Journal of Computer Science and Network Security, 9 (7), July 2009.
[10] K. Singh, I. Gupta, S. Gupta, SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape, International Journal of Signal Processing, Image Processing and Pattern Recognition, 3 (4), December 2010.
[11] I. El-Rube, M. Kamel , M. Ahmed, Wavelet-based affine invariant shape matching and classification. In: ICIP Internat. Conf. on Image Processing, Singapore, 2004.
[12] R. B. Yadav, N. K. Nishchal, A. K. Gupta, V. K. Rastogi, Retrieval and classification of shape-based objects using Fourier, generic Fourier, and wavelet-Fourier descriptors technique: A comparative study, Optics and Lasers in Engineering 45, pp. 695–708, 2007.
[13] F. Mokhtarian, M. Bober, Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization, Kluwer Academic Publishers, Dordrecht, 2003.
[14] Y. K. Liu, W. Wei, P. J. Wang, B. Zalik, Compressed vertex chain codes, Pattern Recognition 40, pp. 2908–2913, 2007.
[15] X. Bai, L.J. Latecki, and W.-Y. Liu, “Skeleton Pruning by Contour Partitioning,” Proc. Int’l Conf. Discrete Geometry for Computer Imagery, pp. 567-579, 2006.
[16] C. Di Ruberto and L. Cinque,” Decomposition of two-dimensional shapes for efficient retrieval” Image and Vision Computing, Vol. 27, pp. 1097–1107, 2009.
[17] E. Attalla, P. Siy, Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching, Pattern Recognition 38, pp. 2229–2241, 2005.
[18] I. Kunttu, L. Lepisto, J. Rauhamaa, A. Visa, Multiscale Fourier descriptors for defect image retrieval, Pattern Recognition Letters 27, pp. 123–132, 2006.
[19] H. Yu, M. Bennamoun, Complete invariants for robust face recognition, Pattern Recognition 40, pp. 1579–1591, 2007.
[20] I.E. Rube, N. Alajlan, M. Kamel, M. Ahmed, G. Freeman, Robust multiscale triangle-area representation for 2D shapes, IEEE International Conference on Image Processing 1, pp. 545–548, 2005.
[21] P. Prakash, V. D. Mytri, P. S. Hiremath, Comparative Analysis of Spectral and Spatial Features for Classification of Graphite Grains in Cast Iron, International Journal of Advanced Science and Technology 29, April 2011.
[22] D. Zhang and G. Lu, A comparative study of curvature scale space and fourier descriptors for shape-based image retrieval, Visual Communication and Image Representation, 14(1), 2003.
[23] I. Kunttu, L. Lepisto, Shape-based retrieval of industrial surface defects using angular radius Fourier descriptor, IET Image Processing 1, pp. 231–236, 2007.
[24] A.E. Oirrak, M. Daoudi, D. Aboutajdin, Affine invariant descriptors using Fourier series, Pattern Recognition Letters 23, pp. 1109–1118, 2002.
[25] I. Bartolini, P. Ciaccia, M. Patella, WARP: accurate retrieval of shapes using phase of Fourier descriptors and time warping distance, IEEE Transactions on Pattern Analysis and Machine Intelligence 27, pp. 142–147, 2005.
[26] D. Rafiei and A.O. Mendelzon, “Efficient Retrieval of Similar Shapes,” The Very Large Data Bases J., vol. 11, no. 1, pp. 17-27, Aug. 2002.
[27] W. Y. Lin, N. Boston & Y. H. Hu. Summation invariant and its applications to shape recognition. Proceedings of IEEE Conference on Acoustics, Speech, and Signal Processing, 5, 205-208, Philadelphia, PA, USA. 2005.
[28] P. Kiran Kumar, S. Das, B. Yegnanarayana.:”One Dimensional processing of images”. International Conference on Multimedia Processing and Systems, pp.181-183, 2000.
[29] I. El-Rube, N. Alajlan, M. Kamel, M. Ahmed, G. Freeman, MTAR: a robust 2D shape representation, International Journal of Image and Graphics 6, pp. 421–443, 2006.
[30] MPEG-7. Subjective Evaluation of the MPEG-7 Retrieval Accuracy Measure (ANMRR). ISO/WG11, Doc. M6029, 2000.
[31] S. Α. Chatzichristofis, C. Iakovidou, Y. S. Boutalis And E. Angelopoulou, "Mean Normalized Retrieval Order (Mnro). A New Content-Based Image Retrieval Performance Measure", «Multimedia Tools And Applications», Springer, Accepted For Publication, 2012. (Doi: 10.1007/S11042-012-1192-Z)

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History

  • Receive Date: 19 April 2014
  • Revise Date: 11 May 2015
  • Accept Date: 27 January 2015

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