Use of Stochastic Optimization Algorithms in Image Retrieval Problems

Chapter

Abstract

In this chapter, the use of stochastic and evolutionary optimization techniques for image retrieval is addressed. We provide background and motivations of such approaches, as well as an overview of some of the most interesting ideas proposed in the recent literature in the field. The relevant methodologies refer to different applications of the optimization process, as a way of either improving the parameter setting in traditional retrieval tools, or directly classifying images within a dataset. Also, the use of stochastic optimization approaches based on social behaviors is discussed, showing how these approaches can be used to capture the semantics of a query through the interaction with the user. Strengths and weaknesses of different solutions are discussed, taking into account also implementation issues, complexity, and open directions of the research.

Keywords

Particle Swarm Optimization Fitness Function Image Retrieval Particle Swarm Optimization Algorithm Stochastic Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Compute 40(2), 1–60 (2008)CrossRefGoogle Scholar
  2. 2.
    Hirata, K., Kato, T.: Query by Visual Example - Content based Image Retrieval. In: Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology, pp. 56–71. Springer, UK (1992)Google Scholar
  3. 3.
    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(12), 1349–1380 (2000)CrossRefGoogle Scholar
  4. 4.
    Hanjalic, A., Lienhart, R., Ma, W.-Y., Smith, J.R.: The Holy Grail of Multimedia Information Retrieval: So Close or Yet So Far Away? JPROC 96(4), 541–547 (2008)Google Scholar
  5. 5.
    Yanai, K., Shirahatti, N.V., Gabbur, P., Barnard, K.: Evaluation strategies for image understanding and retrieval. In: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval (MIR ’05), pp. 217–226. ACM, New York (2005)Google Scholar
  6. 6.
    Deserno, T.M., Antani, S., Long, R.: Ontology of Gaps in Content-Based Image Retrieval. J. Digit. Imaging. (Springer, New York) 22(2), 202–215 (2008)Google Scholar
  7. 7.
    Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Inf. Retrieval (Springer, Netherlands) 11(2), 77–107 (2008)Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision, (Springer, Netherlands) 60(2), 91–110 (2004)Google Scholar
  9. 9.
    Duda, R.O.: Hart . Pattern Classification and Scene Analysis. Wiley, New York (1973)Google Scholar
  10. 10.
    Deng, J., Berg, A., Li, K., Fei-Fei, L.: What does classifying more than 10,000 image categories tell us?. In: Proceedings of European Conference of Computer Vision (ECCV) (2010)Google Scholar
  11. 11.
    Russakovsky, O., Fei-Fei, L.: Attribute learning in large-scale datasets. In: Proceedings of European Conference of Computer Vision (ECCV), International Workshop on Parts and Attributes (2010)Google Scholar
  12. 12.
    Jain, R., Sinha, P.: Content without context is meaningless. In: Proceedings of the international conference on Multimedia (MM ’10), pp. 1259–1268. ACM, New York (2010)Google Scholar
  13. 13.
    Pereira, M.V.F., Pinto, L.M.V.G.: Multi-stage stochastic optimization applied to energy planning. Math. Program. (Springer, Berlin), 52(1), 359–375 (1991)Google Scholar
  14. 14.
    Elbeltagi, E., Hegazy, T., Grierson, D.: Comparison among five evolutionary-based optimization algorithms. Adv. Eng. Inform. 19(1), 43–53 (2005). http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05337717 Google Scholar
  15. 15.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar
  16. 16.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26(1), 29–41 (1996)CrossRefGoogle Scholar
  17. 17.
    Masahiro, I., Jaroslav, R.: Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets Syst. 111(1), 3–28 (2000)MATHCrossRefGoogle Scholar
  18. 18.
    Kennedy, J., Eberhart, R.: Particle-swarm optimization. Proc. Fourth IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)Google Scholar
  19. 19.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  20. 20.
    Eberhart, R., Shi, Y.: Particle-swarm optimization: developments, applications and resources. Proc. Congress Evol. Comput. 1, 81–86 (2001)Google Scholar
  21. 21.
    Clerc, M., Kennedy, J.: The particle-swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)CrossRefGoogle Scholar
  22. 22.
    Hinchey, M.G., Sterritt, R., Rouff, C.: Swarms and swarm intelligence. Computer 40(4), 111–113 (2007)CrossRefGoogle Scholar
  23. 23.
    Robinson, J., Rahmat-Samii, Y.: Particle-swarm optimization in electromagnetics. IEEE Trans. Antennas Propag. 52(2), 397–407 (2004)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Zheng, Y.L., Ma, L.H., Zhang, L.Y., Qian, J.X.: On the convergence analysis and parameter selection in particle-swarm optimization. Int. Conf. Mach. Learn. Cybern. 3, 1802–1807 (2003)Google Scholar
  25. 25.
    Chao, X., Chengjian, W., Jun, X.: Evolutionary wavelet-based similarity search in image databases. In: Proceedings of IEEE International Workshop on VLSI Design and Video Technology, pp. 385–388 (2005)Google Scholar
  26. 26.
    Ye, Z., Xia, B., Wang, D., Zhou, X.: Weight optimization of image retrieval based on particle-swarm optimization algorithm. In: International Symposium on Computer Network and Multimedia Technology, pp. 1–3 (2009)Google Scholar
  27. 27.
    Kameyama, K., Oka, N., Toraichi, K.: Optimal parameter selection in image similarity evaluation algorithms using particle-swarm optimization. In: IEEE Congress on, Evolutionary Computation, pp. 1079–1086 (2006)Google Scholar
  28. 28.
    Okayama, M., Oka, N., Kameyama, K.: Relevance optimization in image database using feature space preference mapping and particle-swarm optimization. Lect. Notes Comput. Sci. Neural Inf. Process. (Springer, Berlin), 4985, 608–617 (2008)Google Scholar
  29. 29.
    Oka, N., Kameyama, K.: Relevance tuning in content-based retrieval of structurally-modeled images using Particle-Swarm Optimization. In: IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, pp. 75–82 (2009)Google Scholar
  30. 30.
    Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling by relaxation operations. IEEE Trans. Syst. Man Cybern. 6(6), 420–433 (1976)MathSciNetMATHCrossRefGoogle Scholar
  31. 31.
    Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  32. 32.
    Chandramouli, K., Izquierdo, E.: Image classification using chaotic particle-swarm optimization. In: IEEE International Conference on Image Processing, pp. 3001–3004 (2006)Google Scholar
  33. 33.
    Piatrik, T., Chandramouli, K., Izquierdo, E.: Image classification using biologically inspired systems. In: Proceedings of the 2nd International Conference on Mobile Multimedia Communications (MobiMedia ’06). ACM, New York (2006)Google Scholar
  34. 34.
    Chandramouli, K.: Particle-swarm optimisation and self organising maps based image classifier. In: Second International Workshop on Semantic Media Adaptation and Personalization, pp. 225–228 (2007)Google Scholar
  35. 35.
    Su, S.: Image classification based on particle-swarm optimization combined with K-means. In: International Conference on Test and Measurement, ICTM ’09, 2, pp. 367–370 (2009)Google Scholar
  36. 36.
    Hung, C.C., Wan, L.: Hybridization of particle-swarm optimization with the K-Means algorithm for image classification. In: IEEE Symposium on Computational Intelligence for Image Processing, CIIP ’09, pp. 60–64 (2009)Google Scholar
  37. 37.
    Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 1055–1064 (1999)CrossRefGoogle Scholar
  38. 38.
    Zhang, Y., Xie, X., Cheng, T.: Application of PSO and SVM in image classification. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 6, pp. 629–631 (2010)Google Scholar
  39. 39.
    Chang, C.Y., Lai, C.T., Chen, S.J.: Applying the particle-swarm optimization and boltzmann function for feature selection and classification of lymph node in ultrasound images. ISDA ’08. In: Eighth International Conference on Intelligent Systems Design and Applications 1, pp. 55–60 (2008)Google Scholar
  40. 40.
    Ding, S., Chen, L.: Classification of hyperspectral remote sensing images with support vector machines and particle-swarm optimization. In: ICIECS International Conference on Information Engineering and Computer Science, pp. 1–5 (2009)Google Scholar
  41. 41.
    Linyi, L., Deren, L.: Fuzzy classification of remote sensing images based on particle-swarm optimization. In: International Conference on Electrical and Control Engineering (ICECE), pp. 1039–1042 (2010)Google Scholar
  42. 42.
    Xu, X., Zhang, A.: An unsupervised particle-swarm optimization classifier for SAR image. Int. Conf. Computational Intell. Secur. 2, 1630–1634 (2006)Google Scholar
  43. 43.
    Zhang, Q., Gao, L.: Medical image retrieval algorithm using setting up weight automatically. In: 3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS), pp. 60–63 (2010)Google Scholar
  44. 44.
    Broilo, M., Rocca, P., De Natale, F.G.B.: Content-based image retrieval by a semi-supervised Particle-Swarm Optimization. In: IEEE 10th Workshop on Multimedia, Signal Processing, pp. 666–671 (2008)Google Scholar
  45. 45.
    Broilo, M., De Natale, F.G.B.: Evolutionary image retrieval. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 1845–1848 (2009)Google Scholar
  46. 46.
    Broilo, M., De Natale, F.G.B.: A stochastic approach to image retrieval using relevance feedback and particle-swarm optimization. IEEE Trans. Multimedia 12(4), 267–277 (2010)CrossRefGoogle Scholar
  47. 47.
    Luo, T., Yuan, B., Tan, L.: Blocking wavelet-histogram image retrieval by adaptive particle-swarm optimization. In: 1st International Conference on Information Science and Engineering (ICISE), pp. 3985–3988 (2009)Google Scholar
  48. 48.
    Luo, T., He, J.: Fast similarity search with blocking wavelet-histogram and adaptive particle-swarm optimization. In: Third International Conference on Knowledge Discovery and Data Mining, WKDD ’10, pp. 334–337 (2010)Google Scholar
  49. 49.
    Xu, X., Liu, X., Yu, Z., Zhou, C., Zhang, L.: Re-weighting relevance feedback image retrieval algorithm based on particle-swarm optimization. Sixth Int. Conf. Nat. Comput. (ICNC) 7, 3609–3613 (2010)Google Scholar
  50. 50.
    Xu, X., Zhang, L., Yu, Z., Zhou, C.: The application of particle-swarm optimization in relevance feedback. In: FBIE International Conference on Future BioMedical Information, Engineering pp. 156–159 (2009)Google Scholar
  51. 51.
    Wei, K., Lu, T., Zhang, Q., Bi, W.: Research of image retrieval algorithm based on PSO and a new sub-block idea. In: 2nd International Conference on Advanced Computer Control (ICACC), 1, pp. 431–435 (2010)Google Scholar
  52. 52.
    Wei, K., Lu, T., Bi, W., Sheng, H.: A kind of feedback image retrieval algorithm based on PSO, Wavelet and subblock sorting thought. In: 2nd International Conference on Future Computer and Communication (ICFCC), 1, pp. V1–796-V1-801 (2010)Google Scholar
  53. 53.
    Wu, F., Li, Y. X., Xu, P., Liang, X.: Image Retrieval Using Ellipse Shape Feature with Particle-Swarm Optimization. In: International Conference on Multimedia Technology (ICMT), pp. 1–4 (2010)Google Scholar
  54. 54.
    Chandramouli, K., Kliegr, T., Nemrava, J., Svatek, V., Izquierdo, E.: Query refinement and user relevance feedback for contextualized image retrieval. In: VIE 5th International Conference on Visual Information, Engineering, pp. 453–458 (2008)Google Scholar
  55. 55.
    Chandramouli, K., Izquierdo, E.: Multi-class relevance feedback for collaborative image retrieval. In: 10th Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS ’09, pp. 214–217 (2009)Google Scholar
  56. 56.
    Ibrahim, S.N.A., Selamat, A., Selamat, M.H.: Query optimization in relevance feedback using hybrid GA-PSO for effective web information retrieval. In: Third Asia International Conference on Modelling and Simulation, AMS ’09, pp. 91–96 (2009)Google Scholar
  57. 57.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston, MA (1989)MATHGoogle Scholar
  58. 58.
    Picard, D., Cord, M., Revel, A.: Image retrieval over networks: active learning using ant algorithm. IEEE Trans. Multimedia 10(7), 1356–1365 (2008)CrossRefGoogle Scholar
  59. 59.
    Piatrik, T., Izquierdo, E.: Subspace clustering of images using Ant colony Optimisation. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 229–232 (2009)Google Scholar
  60. 60.
    Liu, X., Li, X., Liu, L., He, J., Ai, B.: An innovative method to classify remote-sensing images using ant colony optimization. IEEE Trans. Geosci. Remote Sens. 46(12), 4198–4208 (2008)CrossRefGoogle Scholar
  61. 61.
    Kato, S., Iisaku, S.I.: An image retrieval method based on a genetic algorithm. In: Proceedings of Twelfth International Conference on Information Networking (ICOIN-12), pp. 333–336 (1998)Google Scholar
  62. 62.
    Papadias, D., Mantzourogiannis, M., Ahmad, I.: Fast retrieval of similar configurations. IEEE Trans. Multimedia 5(2), 210–222 (2003)CrossRefGoogle Scholar
  63. 63.
    Cho, S.B., Lee, J.Y.: A human-oriented image retrieval system using interactive genetic algorithm. Syst. Man Cybernetics Part A IEEE Trans. Syst. Hum. 32(3), 452–458 (2002)Google Scholar
  64. 64.
    Tran, K. D.: Content-based retrieval using a multi-objective genetic algorithm. In: IEEE Proceedings of the SoutheastCon, pp. 561–569 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.DISIUniversity of TrentoTrentoItaly

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