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Optimizing Query Perturbations to Enhance Shape Retrieval

  • Bilal MokhtariEmail author
  • Kamal Eddine Melkemi
  • Dominique Michelucci
  • Sebti Foufou
Conference paper
  • 27 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11989)

Abstract

3D Shape retrieval algorithms use shape descriptors to identify shapes in a database that are the most similar to a given key shape, called the query. Many shape descriptors are known but none is perfect. Therefore, the common approach in building 3D Shape retrieval tools is to combine several descriptors with some fusion rule. This article proposes an orthogonal approach. The query is improved with a Genetic Algorithm. The latter makes evolve a population of perturbed copies of the query, called clones. The best clone is the closest to its closest shapes in the database, for a given shape descriptor. Experimental results show that improving the query also improves the precision and completeness of shape retrieval output. This article shows evidence for several shape descriptors. Moreover, the method is simple and massively parallel.

Keywords

Computer vision 3D Shape matching and recognition Shape Retrieval Shape Descriptors Cloning Genetic Algorithms 

References

  1. 1.
    Akgül, C.B., Sankur, B., Yemez, Y., Schmitt, F.: Similarity score fusion by ranking risk minimization for 3D object retrieval. In: Proceedings of the 1st Eurographics Conference on 3D Object Retrieval, pp. 41–48. Eurographics Association (2008)Google Scholar
  2. 2.
    Aparna, K.: Retrieval of digital images based on multi-feature similarity using genetic algorithm. Int. J. Eng. Res. Appl. (IJERA) 3(4), 1486–1499 (2013)Google Scholar
  3. 3.
    Bronstein, M.M., Kokkinos, I.: Scale-invariant heat kernel signatures for non-rigid shape recognition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1704–1711. IEEE (2010)Google Scholar
  4. 4.
    Bu, S., Cheng, S., Liu, Z., Han, J.: Multimodal feature fusion for 3D shape recognition and retrieval. IEEE MultiMedia 21(4), 38–46 (2014)CrossRefGoogle Scholar
  5. 5.
    Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394–410 (2007)CrossRefGoogle Scholar
  6. 6.
    Chahooki, M., Charkari, N.M.: Shape retrieval based on manifold learning by fusion of dissimilarity measures. IET Image Process. 6(4), 327–336 (2012)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Chan, D.Y.-M., King, I.: Genetic algorithm for weights assignment in dissimilarity function for trademark retrieval. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 557–565. Springer, Heidelberg (1999).  https://doi.org/10.1007/3-540-48762-X_69CrossRefGoogle Scholar
  8. 8.
    Chang, M.C., Kimia, B.B.: Measuring 3D shape similarity by graph-based matching of the medial scaffolds. Comput. Vis. Image Underst. 115(5), 707–720 (2011). Special issue on 3D Imaging and ModellingCrossRefGoogle Scholar
  9. 9.
    Chang, S.K., Wong, Y.: Ln norm optimal histogram matching and application to similarity retrieval. Comput. Graph. Image Process. 13(4), 361–371 (1980)CrossRefGoogle Scholar
  10. 10.
    Chao, M.W., Lin, C.H., Chang, C.C., Lee, T.Y.: A graph-based shape matching scheme for 3D articulated objects. Comput. Animation Virtual Worlds 22(2–3), 295–305 (2011)CrossRefGoogle Scholar
  11. 11.
    Chapeau-Blondeau, F., Rousseau, D.: Raising the noise to improve performance in optimal processing. J. Stat. Mech. Theory Exp. 2009(01), P01003 (2009)CrossRefGoogle Scholar
  12. 12.
    Chen, D.Y., Ouhyoung, M.: A 3D object retrieval system based on multi-resolution Reeb graph. In: Computer Graphics Workshop, pp. 16–20 (2002)Google Scholar
  13. 13.
    Chen, H., Varshney, L.R., Varshney, P.K.: Noise-enhanced information systems. Proc. IEEE 102(10), 1607–1621 (2014)CrossRefGoogle Scholar
  14. 14.
    Coifman, R., et al.: Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Nat. Acad. Sci. 102(21), 7426–7431 (2005)CrossRefzbMATHGoogle Scholar
  15. 15.
    De Berg, M., Van Kreveld, M., Overmars, M., Schwarzkopf, O.C.: Computational Geometry. Springer, Heidelberg (2000).  https://doi.org/10.1007/978-3-662-04245-8CrossRefzbMATHGoogle Scholar
  16. 16.
    Emiris, I.Z., Canny, J.F.: A general approach to removing degeneracies. SIAM J. Comput. 24(3), 650–664 (1995)CrossRefMathSciNetzbMATHGoogle Scholar
  17. 17.
    Ernest, N., Cohen, K., Kivelevitch, E., Schumacher, C., Casbeer, D.: Genetic fuzzy trees and their application towards autonomous training and control of a squadron of unmanned combat aerial vehicles. Unmanned Syst. 3(03), 185–204 (2015)CrossRefGoogle Scholar
  18. 18.
    Fan, W., Gordon, M.D., Pathak, P.: A generic ranking function discovery framework by genetic programming for information retrieval. Inf. Process. Manage. 40(4), 587–602 (2004)CrossRefzbMATHGoogle Scholar
  19. 19.
    Fang, Y., Sun, M., Ramani, K.: Temperature distribution descriptor for robust 3D shape retrieval. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9–16. IEEE (2011)Google Scholar
  20. 20.
    Gal, R., Cohen-Or, D.: Salient geometric features for partial shape matching and similarity. ACM Trans. Graph. 25(1), 130–150 (2006)CrossRefGoogle Scholar
  21. 21.
    Goldberg, D.E., et al.: Genetic Algorithms in Search Optimization and Machine Learning, vol. 412. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  22. 22.
    Hoffmann, P.H.C.M., Revol, W.L.N. (eds.): Reliable Implementation of Real Number Algorithms: Theory and Practice. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-85521-7CrossRefGoogle Scholar
  23. 23.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)zbMATHGoogle Scholar
  24. 24.
    Karbasi, A., Salavati, A.H., Shokrollahi, A., Varshney, L.R.: Noise facilitation in associative memories of exponential capacity. Neural Comput. 26(11), 2493–2526 (2014)CrossRefGoogle Scholar
  25. 25.
    Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  26. 26.
    Li, B., et al.: SHREC’12 track: generic 3D shape retrieval. In: 3DOR, pp. 119–126 (2012)Google Scholar
  27. 27.
    Li, B., Godil, A., Johan, H.: Hybrid shape descriptor and meta similarity generation for non-rigid and partial 3D model retrieval. Multimedia Tools Appl. 72(2), 1531–1560 (2014)CrossRefGoogle Scholar
  28. 28.
    Ling, H., Jacobs, D.W.: Using the inner-distance for classification of articulated shapes. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 719–726. IEEE (2005)Google Scholar
  29. 29.
    Luo, J., Gu, F.: An adaptive niching-based evolutionary algorithm for optimizing multi-modal function. Int. J. Pattern Recognit. Artif. Intell. 30(03), 1659007 (2016)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Lv, J.J., Cheng, C., Tian, G.D., Zhou, X.D., Zhou, X.: Landmark perturbation-based data augmentation for unconstrained face recognition. Signal Process. Image Commun. 47, 465–475 (2016)CrossRefGoogle Scholar
  31. 31.
    Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  32. 32.
    Meyer, M., Desbrun, M., Schröder, P., Barr, A.H.: Discrete differential-geometry operators for triangulated 2-manifolds. In: Hege, H.C., Polthier, K. (eds.) Visualization and Mathematics III, pp. 35–57. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-662-05105-4_2CrossRefGoogle Scholar
  33. 33.
    Miranda, V., Ranito, J., Proenca, L.M.: Genetic algorithms in optimal multistage distribution network planning. IEEE Trans. Power Syst. 9(4), 1927–1933 (1994)CrossRefGoogle Scholar
  34. 34.
    Misevičius, A.: Experiments with hybrid genetic algorithm for the grey pattern problem. Informatica 17(2), 237–258 (2006)zbMATHGoogle Scholar
  35. 35.
    Misevičius, A., Rubliauskas, D.: Testing of hybrid genetic algorithms for structured quadratic assignment problems. Informatica 20(2), 255–272 (2009)zbMATHGoogle Scholar
  36. 36.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  37. 37.
    Mohamad, M.S., Deris, S., Illias, R.M.: A hybrid of genetic algorithm and support vector machine for features selection and classification of gene expression microarray. Int. J. Comput. Intell. Appl. 5(01), 91–107 (2005)CrossRefGoogle Scholar
  38. 38.
    Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Trans. Graph. (TOG) 21(4), 807–832 (2002)CrossRefMathSciNetzbMATHGoogle Scholar
  39. 39.
    Pele, O., Werman, M.: The Quadratic-Chi histogram distance family. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 749–762. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15552-9_54CrossRefGoogle Scholar
  40. 40.
    Rousseau, D., Anand, G., Chapeau-Blondeau, F.: Noise-enhanced nonlinear detector to improve signal detection in non-Gaussian noise. Signal Process. 86(11), 3456–3465 (2006)CrossRefzbMATHGoogle Scholar
  41. 41.
    Safar, M.H., Shahabi, C.: Shape Analysis and Retrieval of Multimedia Objects, vol. 23. Springer, Boston (2003).  https://doi.org/10.1007/978-1-4615-0349-1CrossRefGoogle Scholar
  42. 42.
    Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Computer Graphics Forum, pp. 1383–1392. Wiley Online Library (2009)Google Scholar
  43. 43.
    Super, B.J.: Retrieval from shape databases using chance probability functions and fixed correspondence. Int. J. Pattern Recognit. Artif. Intell. 20(08), 1117–1137 (2006)CrossRefGoogle Scholar
  44. 44.
    Syam, B., Rao, Y.: An effective similarity measure via genetic algorithm for content based image retrieval with extensive features. Int. Arab J. Inf. Technol. (IAJIT) 10(2), 143–151 (2013)Google Scholar
  45. 45.
    Thada, V., Jaglan, V.: Comparison of Jaccard, Dice, Cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm. Int. J. Innov. Eng. Technol. 2(4), 202–205 (2013)Google Scholar
  46. 46.
    Thompson, J., Flynn, P.: A segmentation perturbation method for improved iris recognition. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–8, September 2010Google Scholar
  47. 47.
    Thürrner, G., Wüthrich, C.A.: Computing vertex normals from polygonal facets. J. Graph. Tools 3(1), 43–46 (1998)CrossRefzbMATHGoogle Scholar
  48. 48.
    Vaira, G., Kurasova, O.: Genetic algorithm for VRP with constraints based on feasible insertion. Informatica 25(1), 155–184 (2014)CrossRefMathSciNetGoogle Scholar
  49. 49.
    Vignes, J.: A stochastic arithmetic for reliable scientific computation. Math. Comput. Simul. 35(3), 233–261 (1993)CrossRefMathSciNetGoogle Scholar
  50. 50.
    Wong, W.T., Shih, F.Y., Su, T.F.: Shape-based image retrieval using two-level similarity measures. Int. J. Pattern Recognit. Artif. Intell. 21(06), 995–1015 (2007)CrossRefGoogle Scholar
  51. 51.
    Yang, X., Bai, X., Latecki, L.J., Tu, Z.: Improving shape retrieval by learning graph transduction. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 788–801. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88693-8_58CrossRefGoogle Scholar
  52. 52.
    Yin, G., Rudolph, G., Schwefel, H.P.: Establishing connections between evolutionary algorithms and stochastic approximation. Informatica 6(1), 93–117 (1995)MathSciNetzbMATHGoogle Scholar
  53. 53.
    Young, S., Adelstein, B., Ellis, S.: Calculus of nonrigid surfaces for geometry and texture manipulation. IEEE Trans. Visual Comput. Graphics 13(5), 902–913 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Bilal Mokhtari
    • 1
    Email author
  • Kamal Eddine Melkemi
    • 2
  • Dominique Michelucci
    • 3
  • Sebti Foufou
    • 3
    • 4
  1. 1.Laboratory of Applied Mathematics LMAUniversity of BiskraBiskraAlgeria
  2. 2.Department of Computer ScienceUniversity of Batna 2BatnaAlgeria
  3. 3.Laboratoire d’Informatique de Bourgogne, EA 7534Université de BourgogneDijon CedexFrance
  4. 4.New York University of Abu DhabiAbu DhabiUAE

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