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Learning the shape manifold to improve object recognition

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Abstract

This paper presents an approach for object recognition and shape retrieval in binary images. Object description is accomplished by contour and region-based solutions. Fusion of two categories of shape descriptors causes a considerable performance improvement in retrieval. In order to improve the recognition rate, it is important to extract the intrinsic object feature vectors in such a way that the geometrical distance between two such vectors matches the semantic relation between the two objects from which they were extracted. The main contribution in this paper is learning how to map the samples in high-dimensional observation space into the new manifold space so that the geometrically closer vectors belong to near semantics. Feature extraction is done by Isomap that belongs to a non-linear feature extraction algorithm. In the proposed method, the shortest path in the dissimilarity graph leads to less value between the samples in the same category by denoising the edge values. Experiments show that the geometrical distance between the samples on the manifold space are more compatible to the semantic distance of them. The proposed method has been compared to some well-known approaches by a variety of shape databases which includes MPEG-7 Part B, Kimia silhouettes, and Fish. The results show the effectiveness and validity of the method.

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References

  1. Liu Y., Zhang D., Lu G., Ma W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40, 262–282 (2007)

    Article  MATH  Google Scholar 

  2. Alajlan N., El Rube I., Kamel M.S., Freeman G.: Shape retrieval using triangle-area representation and dynamic space warping. Pattern Recogn. 40, 1911–1920 (2007)

    Article  MATH  Google Scholar 

  3. Yang, M., Kpalma, K., Ronsin, J.: A survey of shape feature extraction techniques. Pattern Recogn. Peng-Yeng Yin, 43–90 (2008)

  4. Avrithis Y., Xirouhakis Y., Kollias S.: Affine-invariant curve normalization for object shape representation, classification and retrieval. Mach. Vis. Appl. 13, 80–94 (2001)

    Article  Google Scholar 

  5. Çapar A., Kurt B., Gökmen M.: Gradient-based shape descriptors. Mach. Vis. Appl. 20, 365–378 (2009)

    Article  Google Scholar 

  6. Lu G., Sajjanhar A.: Region-based shape representation and similarity measure suitable for content-based image retrieval. Multimedia Syst. 7, 165–174 (1999)

    Article  Google Scholar 

  7. Qi H., Li K., Shen Y., Qu W.: An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recogn. 43, 2017–2027 (2010)

    Article  MATH  Google Scholar 

  8. Ling H., Jacobs D.W.: Shape classification using the inner-distance. IEEE Trans. Pattern Anal. Mach. Intell. 29, 286–299 (2007)

    Article  Google Scholar 

  9. 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, 1501–1516 (2002)

    Article  Google Scholar 

  10. Huang X., Wang B., Zhang L.: A new scheme for extraction of affine invariant descriptor and affine motion estimation based on independent component analysis. Pattern Recogn. Lett. 26, 1244–1255 (2005)

    Article  Google Scholar 

  11. Veerabhadrappam, M., Rangarajan, L.: Diagonal locality preserving projection as dimensionality reduction technique with application to face recognition. Int. J. Comput. Appl. Special Issue on Recent Trends in Image Processing and Pattern Recognition. 135–140 (2010)

  12. Bellman R.: Adaptive Control Processes: a Guided Tour, pp 143–397. Princeton University Press, New Jersey (1961)

    Google Scholar 

  13. Smeulders A.W.M., Worring M., Santini S., Gupta A., Jain R.: Content-based image retrieval at the end of the early years. IEEE Trans. pattern Anal. Mach. Intell. 22, 1349–1380 (2000)

    Article  Google Scholar 

  14. Webb A.: Statistical Pattern Recognition, pp. 305–305. Wiley, England (2002)

    Book  Google Scholar 

  15. Seung H.S., Lee D.D.: The manifold ways of perception. Science(Washington) 290, 2268–2269 (2000)

    Article  Google Scholar 

  16. Jordan M., Kleinberg J., Schlkopf B.: Nonlinear Dimensionality Reduction, pp. 11–14. Springer, Berlin (2007)

    Google Scholar 

  17. Zhang Z., Zha H.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. J. Shanghai Univer. (English Edition) 8, 406–424 (2004)

    Article  MathSciNet  Google Scholar 

  18. Kruskal J.B.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1–27 (1964)

    Article  MathSciNet  MATH  Google Scholar 

  19. Shepard R.N.: The analysis of proximities: multidimensional scaling with an unknown distance function. II. Psychometrika 27, 219–246 (1962)

    Article  MathSciNet  Google Scholar 

  20. Tenenbaum J.B.: Mapping a manifold of perceptual observations, Adv. Neural Inf. Process. Syst. 10, 682–688 (1998)

    Google Scholar 

  21. Sammon J.W. Jr: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 100, 401–409 (1969)

    Article  Google Scholar 

  22. Demartines P., Herault J.: Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Trans. Neural Netw. 8, 148–154 (2002)

    Article  Google Scholar 

  23. Tenenbaum J.B., Silva V., Langford J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)

    Article  Google Scholar 

  24. Lee, J.A., Lendasse, A., Donckers, N., Verleysen, M.: A robust nonlinear projection method, European Symposium on Artificial Neural Networks, pp. 13–20 (2000)

  25. Estevez, P.A., Chong, A.M.: Geodesic nonlinear mapping using the neural gas network. International Joint Conference on Neural Networks, pp. 3287–3294 (2006)

  26. Roweis S.T., Saul L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  27. Belkin M., Niyogi P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003)

    Article  MATH  Google Scholar 

  28. He X., Niyogi P.: Locality preserving projections. Adv. Neural Inf. Process. Syst. 16, 153–160 (2003)

    Google Scholar 

  29. Donoho D.L., Grimes C.: Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. 100, 5591–5596 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  30. Weinberger K.Q., Saul L.K.: Unsupervised learning of image manifolds by semidefinite programming. Int. J. Comput. Vis. 70, 77–90 (2006)

    Article  Google Scholar 

  31. He, X., Ma, W.Y., Zhang, H.J.: Learning an image manifold for retrieval. In: 12th annual ACM international conference on Multimedia, pp. 17–23 (2004)

  32. Cai, D., He, X., Han, J.: Regularized regression on image manifold for retrieval. In: ACM International Workshop on Multimedia Information Retrieval, pp. 11–19 (2007)

  33. Yu, J., Tian, Q.: Learning image manifolds by semantic subspace projection. In: 14th annual ACM international conference on Multimedia, pp. 306–315 (2006)

  34. Ohbuchi, R., Kobayashi, J., Yamamoto, A., Shimizu, T.: Comparison of dimension reduction methods for database-adaptive 3D model retrieval. In: AMR 2007, pp. 196–210 (2008)

  35. He, X.: Incremental semi-supervised subspace learning for image retrieval. In: 12th Annual ACM International Conference on Multimedia, pp. 2–8 (2004)

  36. Lin Y.Y., Liu T.L., Chen H.T.: Semantic manifold learning for image retrieval. In: 13th Annual ACM International Conference on Multimedia, pp. 249–258 (2005)

  37. He X., Cai D., Han J.: Learning a maximum margin subspace for image retrieval. IEEE Trans. Knowl. Data Eng. 20, 189–201 (2007)

    Google Scholar 

  38. Zhang D., Lu G.: Study and evaluation of different Fourier methods for image retrieval. Image Vis. Comput. 23, 33–49 (2005)

    Article  MATH  Google Scholar 

  39. El-ghazal A., Basir O., Belkasim S.: Farthest point distance: a new shape signature for Fourier descriptors. Signal Process. Image Commun. 24, 572–586 (2009)

    Article  Google Scholar 

  40. Kim W.Y., Kim Y.S.: A region-based shape descriptor using Zernike moments. Signal Process. Image Commun. 16, 95–102 (2000)

    Article  Google Scholar 

  41. Mei Y., Androutsos D.: Robust affine invariant region-based shape descriptors: the ica zernike moment shape descriptor and the whitening Zernike moment shape descriptor. IEEE Signal Process. Lett. 16, 877–880 (2009)

    Article  Google Scholar 

  42. Khotanzad A., Hong Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12, 489–497 (1990)

    Article  Google Scholar 

  43. Wei C.H., Li Y., Chau W.Y., Li C.T.: Trademark image retrieval using synthetic features for describing global shape and interior structure. Pattern Recogn. 42, 386–394 (2009)

    Article  MATH  Google Scholar 

  44. Kim H.K., Kim J.D.: Region-based shape descriptor invariant to rotation, scale and translation. Signal Process. Image Commun. 16, 87–93 (2000)

    Article  Google Scholar 

  45. Liu, H., Song, D., Rger, S., Hu, R., Uren, V.: Comparing dissimilarity measures for content-based image retrieval. In: 4th Asia Information Retrieval Symposium(AIRS), pp. 44–50 (2008)

  46. Geng X., Zhan D., Zhou Z.: Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans. Syst. Man Cybern. 35, 1098–1107 (2005)

    Article  Google Scholar 

  47. Belongie S., Malik J., Puzicha J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 509–522 (2002)

    Article  Google Scholar 

  48. Sebastian, T.B.,Klein, P.N., Kimia, B.B.:On aligning curves. IEEE Trans. Pattern Anal. Mach. Intell. 25, 116–125 (2003)

    Article  Google Scholar 

  49. Grigorescu, C., Petkov,N.: Distance sets for shape filters and shape recognition. IEEE Trans. Image Process. 12, 1274–1286 (2003)

    Article  MathSciNet  Google Scholar 

  50. Jalba A.C., Wilkinson M.H.F., Roerdink J.: Shape representation and recognition through morphological curvature scale spaces. IEEE Trans. Image Process. 15, 331–341 (2006)

    Article  Google Scholar 

  51. Mokhtarian F., Bober M.: Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization. p. 25, Kluwer, Dordrecht (2003)

    MATH  Google Scholar 

  52. Arica N., Yarman Vural F.T.: BAS: a perceptual shape descriptor based on the beam angle statistics. Pattern Recogn. Lett. 24, 1627–1639 (2003)

    Article  MATH  Google Scholar 

  53. Adamek T., OConnor N.E.: A multiscale representation method for nonrigid shapes with a single closed contour. IEEE Trans. Circuits Syst. Video Technol. 14, 742–753 (2004)

    Article  Google Scholar 

  54. Bandera A., Antnez E., Marfil R.: An adaptive approach for affine-invariant 2D shape description. Pattern Recogn. Image Anal. 5524, 417–424 (2009)

    Article  Google Scholar 

  55. Alajlan N., Kamel M.S., Freeman G.H.: Geometry-based image retrieval in binary image databases. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1003–1013 (2008)

    Article  Google Scholar 

  56. Yang, X., Bai, X., Latecki, L., Tu, Z.: Improving shape retrieval by learning graph transduction. Computer VisionECCV, 788–801 (2008)

  57. Sebastian T.B., Klein P.N., Kimia B.B.: Recognition of shapes by editing their shock graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26, 550–571 (2004)

    Article  Google Scholar 

  58. Sharvit D., Chan J., Tek H., Kimia B.: Symmetry-based indexing of image database. Vis. Commun. Image Represent. 9, 366–380 (1998)

    Article  Google Scholar 

  59. Mokhtarian, F., Abbasi, S., Kittler, J.: Robust and efficient shape indexing through curvature scale space. Proc. British Mach. Vis. Confer. 53–62 (1996)

  60. Gdalyahu Y., Weinshall D.: Flexible syntactic matching of curves and its application to automatic hierarchical classification of Silhouettese. IEEE Trans. Pattern Anal. Mach. Intell. 21, 1312–1328 (1999)

    Article  Google Scholar 

  61. Belongie S., Malik J., Puzicha J.: Shape matching and object recognition using shape context. IEEE Trans. Pattern Anal. Mach. Intell. 24, 509–522 (2002)

    Article  Google Scholar 

  62. Tu, Z., Yuille, A.L.: Shape matching and recognition-using generative models and informative features. Eur. Confer. Comput. Vis. 195–209 (2004)

  63. 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, 142–147 (2005)

    Article  Google Scholar 

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  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Mohammad Ali Zare Chahooki & Nasrollah Moghadam Charkari

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  1. Mohammad Ali Zare Chahooki
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  2. Nasrollah Moghadam Charkari
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Correspondence to Nasrollah Moghadam Charkari.

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Chahooki, M.A.Z., Charkari, N.M. Learning the shape manifold to improve object recognition. Machine Vision and Applications 24, 33–46 (2013). https://doi.org/10.1007/s00138-011-0400-6

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