A hybrid multi-objective optimization algorithm for content based image retrieval

Applied Soft Computing Journal

Volumn 13, Issue 11, 2013, Pages 4358-4369

  Arevalillo Herráez, Miguel ^a   Ferri, Francesc J ^a   Moreno Picot, Salvador ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

CBIR; Image retrieval; NSGA II multi objective; Relevance feedback

Indexed keywords

CONTENT BASED RETRIEVAL; GENETIC ALGORITHMS; ITERATIVE METHODS; LEARNING ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; OPTIMIZATION;

CBIR; CONTENT BASED IMAGE RETRIEVAL; MULTI OBJECTIVE; NON- DOMINATED SORTING GENETIC ALGORITHMS; PRECISION AND RECALL; REGIONS OF INTEREST; RELEVANCE FEEDBACK; RELEVANCE FEEDBACK METHOD;

IMAGE RETRIEVAL;

EID: 84885379650     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2013.06.016     Document Type: Article

References (48)

1. F. Long, H. Zhang, D. Feng, Multimedia Information Retrieval and Management. Technological Fundamentals and Applications, Springer-Verlag, Berlin/Heidelberg/New York, 2003, pp. 1-26, Ch. Fundamentals of contentbased image retrieval.
2. N. de Freitas, E. Brochu, K. Barnard, P. Duygulu, D. Forsyth, Bayesian Models for Massive Multimedia Databases: A New Frontier, Tech. Rep. TR-2003-5, Department of Computer Science, University of British Columbia, 2003.
3. N. Vasconcelos, A. Lippman, Learning from user feedback in image retrieval systems, in: Proceedings of Advances in Neural Information Processing Systems (NIPS'99), Denver, Colo, USA, 1999, pp. 977-986.
4. Z. Su, H.J. Zhang, S. Li, S. Ma, Relevance feedback in content-based image retrieval: Bayesian framework, feature sub-spaces, and progressive learning, in: IEEE Transactions Image Processing, vol. 12, 2003, pp. 924-937.
5. A. Marakakis, N. Galatsanos, A. Likas, A. Stafylopatis, Probabilistic relevance feedback approach for content-based image retrieval based on gaussian mixture models, IET Image Processing 3 (1) (2009) 10-25.
6. G. Giacinto, A nearest-neighbor approach to relevance feedback in content based image retrieval, in: Proceedings of the 6th ACM International Conference on Image and Video Retrieval (CIVR'07), Amsterdam, The Netherlands, ACM Press, 1993, pp. 456-463.
7. X.S. Zhou, T.S. Huang, Small sample learning during multimedia retrieval using biasmap, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001, pp. 11-17.
8. X.-Y. Wang, J.-W. Chen, H.-Y. Yang, A new integrated SVM classifiers for relevance feedback content-based image retrieval using em parameter estimation, Applied Soft Computing 11 (2) (2011) 2787-2804.
9. L. Zhang, L. Wang, W. Lin, Biased subspace learning for SVM relevance feedback in content-based image retrieval, in: 8th International Conference on Information, Communications and Signal Processing (ICICS) 2011, 2011, pp. 1-5.
10. I. Gondra, R. Heisterkamp, J. Peng, Improving image retrieval performance by inter-query learning with one-class support vector machines, Neural Computing and Applications 13 (2) (2004) 130-139. (Pubitemid 38921915)
11. D. Tao, X. Tang, X. Li, Which components are important for interactive image searching? IEEE Transactions on Circuits and Systems for Video Technology 18 (1) (2008) 3-11.
12. J. Wu, M. Lu, Asymmetric bayesian learning for image retrieval with relevance feedback, in: S. Boll, Q. Tian, L. Zhang, Z. Zhang, Y.-P. Chen (Eds.), Advances in Multimedia Modeling, Vol. 5916 of Lecture Notes in Computer Science, Springer, Berlin/Heidelberg, 2010, pp. 650-655.
13. S.C. Hoi, W. Liu, S.-F. Chang, Semi-supervised distance metric learning for collaborative image retrieval and clustering, ACM Transactions on Multimedia Computing, Communications and Applications 6 (3) (2010), 18:1-18:26.
14. Z.-H. Zhou, M. Li, Semi-supervised learning by disagreement, Knowledge and Information Systems 24 (2010) 415-439.
15. D. Liu, K. Hua, K. Vu, N. Yu, Fast query point movement techniques for large CBIR systems, IEEE Transactions on Knowledge and Data Engineering 1 (5) (2009) 729-743.
16. G. Ciocca, R. Schettini, A relevance feedback mechanism for content-based image retrieval, Information Processing and Management 35 (1) (1999) 605-632. (Pubitemid 30508640)
17. J. Chen, R. Ma, Z. Su, Weighting visual features with pseudo relevance feedback for CBIR, in: Proceedings of the ACM International Conference on Image and Video Retrieval, CIVR'10, ACM, New York, NY, USA, 2010, pp. 220-227.
18. C.-Y. Chiu, H.-C. Lin, S.-N. Yang, A fuzzy logic CBIR system, in: The 12th IEEE International Conference on Fuzzy Systems, vol. 2, 2003, pp. 1171-1176.
19. M. Arevalillo-Herráez, M. Zacarés, X. Benavent, E. de Ves, A relevance feedback CBIR algorithm based on fuzzy sets, Signal Processing: Image Communication Special Issue on Semantic Analysis for Interactive Multimedia Services (7) (2008) 490-504.
20. J. Laaksonen, M. Koskela, E. Oja, Picsomself-organizing image retrieval with mpeg-7 content descriptors, IEEE Transactions on Neural Networks 13 (4) (2002) 841-853.
21. Y. Chen, J. Wang, R. Krovetz, Clue: cluster-based retrieval of images by unsupervised learning, IEEE Transactions on Image Processing 14 (8) (2005) 1187-1201. (Pubitemid 41236147)
22. O. Cordón, E. Herrera-Viedma, C. Lpez-Pujalte, M. Lunque, C. Zarco, A review on the application of evolutionary computation to information retrieval, International Journal of Approximate Reasoning 34 (2003) 241-264.
23. M. Saadatmand-Tarzjan, H. Abrishami Moghaddam, A novel evolutionary approach for optimizing content-based image indexing algorithms, IEEE Transactions on Systems, Man, and Cybernetics - Part B 37 (1) (2007) 139-153. (Pubitemid 46370385)
24. M. Arevalillo-Herráez, F.J. Ferri, S. Moreno-Picot, Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval, Applied Soft Computing 11 (2) (2011) 1782-1791.
25. K. Deb, D. Kalyanmoy, Multi-Objective Optimization Using Evolutionary Algorithms, 1st Edition, Wiley, New York, 2001.
26. K. Miettinen, Nonlinear Multiobjective Optimization, 1st Edition, Kluwer Academic Publishers, Norwell, MA, 1999.
27. M. Ehrgott, Multicriteria optimization, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, 2000.
28. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA, IEEE Transactions on Evolutionary Computation 6 (2) (2002) 182-197. (Pubitemid 34555372)
29. G. Giacinto, F. Roli, Nearest-prototype relevance feedback for content based image retrieval, in: ICPR'04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04), vol. 2, Washington, DC, USA, IEEE Computer Society, 2004, pp. 989-992.
30. G. Smith, I. Burns, Measuring texture classification algorithms, Pattern Recognition Letters 18 (14) (1997) 1495-1501. (Pubitemid 127434846)
31. R.W. Conners, M.M. Trivedi, C.A. Harlow, Segmentation of a high-resolution urban scene using texture operators, Computer Vision, Graphics, and Image Processing 25 (3) (1984) 273-310.
32. R. Chellappa, S. Chatterjee, Classification of textures using gaussian markov random fields, IEEE Transactions on Acoustics Speech and Signal Processing 33 (1985) 959-963. (Pubitemid 16508621)
33. Y. Chen, E. Dougherty, Gray-scale morphological granulometric texture classification, Optical Engineering 33 (8) (1994) 2713-2722.
34. G. Ayala, J. Domingo, Spatial size distributions. applications to shape and texture analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (12) (2001) 1430-1442. (Pubitemid 34035575)
35. M. Arevalillo-Herráez, J. Domingo, F.J. Ferri, Combining similarity measures in content-based image retrieval, Pattern Recognition Letters 29 (16) (2008) 2174-2181.
36. P. Soille, Morphological Image Analysis: Principles and Applications, Springer-Verlag, Berlin, 2003.
37. E. de Ves, X. Benavent, G. Ayala, J. Domingo, Selecting the structuring element for morphological texture classification, Pattern Analysis and Applications 9 (2006) 48-57. (Pubitemid 43823165)
38. Q. Iqbal, J. Aggarwal, Combining structure, color and texture for image retrieval: A performance evaluation, in: 16th International Conference on Pattern Recognition (ICPR), vol. 2, 2002, pp. 438-443. (Pubitemid 44844772)
39. Y. Rui, S. Huang, M. Ortega, S. Mehrotra, Relevance feeback: a power tool for interactive content-based image retrieval, IEEE Transactions on Circuits and Video Technology 8 (5) (1998) 644-655. (Pubitemid 128743730)
40. M.J. Swain, D.H. Ballard, Color indexing, International Journal of Computer Vision 7 (1) (1991) 11-32.
41. K. Dep, R. Agrawal, Simulated binary crossover for continuous search space, Complex Systems 9 (1995) 115-148. (Pubitemid 126034016)
42. K. Deb, M. Goyal, A combined genetic adaptive search (geneas) for engineering design, Computer Science and Informatics 26 (1996) 30-45.
43. J.J. Durillo, A.J. Nebro, jmetal: A java framework for multi-objective optimization, Advances in Engineering Software 42 (2011) 760-771.
44. Y. Chang, C. Cirillo, J. Razor, Evaluation of feedback retrieval using modified freezing, residual collection, and test and control groups, Prentice Hall Inc, Englewood Cliffs, NJ, 1971.
45. G. Salton, C. Buckley, Improving retrieval performance by relevance feedback, Journal of the American Society for Information Science 41 (4) (1990) 288-297.
46. C. López-Pujalte, V.P. Guerrero-Bote, Order-based fitness functions for genetic algorithms applied to relevance feedback, Journal of the American Society for Information Science and Technology 54 (2) (2003) 152-160.
47. B. Thomee, M.S. Lew, Interactive search in image retrieval: a survey, International Journal of Multimedia Information Retrieval 1 (2) (2012) 71-86.
48. G. Ciocca, R. Schettini, Content-based similarity retrieval of trademarks using relevance feedback, Pattern Recognition 34 (8) (2001) 1639-1655. (Pubitemid 32541344)