Table of Contents
ISRN Machine Vision
Volume 2013, Article ID 428746, 16 pages
http://dx.doi.org/10.1155/2013/428746
Research Article

Multimodal Markov Random Field for Image Reranking Based on Relevance Feedback

Department of Computer Sciences, Instituto Nacional de Astrofśsica, Óptica y Electrónica, Luis Enrique Erro No. 1, 72840 Tonantzintla, PUE, Mexico

Received 4 December 2012; Accepted 30 December 2012

Academic Editors: H. Erdogan, N. Grammalidis, N. D. A. Mascarenhas, and W. L. Woo

Copyright © 2013 Ricardo Omar Chávez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-based image retrieval at the end of the early years,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349–1380, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Goodrum, “Image information retrieval: an overview of current research,” Journal of Informing Science, vol. 3, no. 2, pp. 63–66, 2000. View at Google Scholar
  3. R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: ideas, influences, and trends of the new age,” ACM Computing Surveys, vol. 40, no. 2, article 5, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Liu, D. Zhang, G. Lu, and W. Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recognition, vol. 40, no. 1, pp. 262–282, 2007. View at Google Scholar
  5. M. S. Lew, N. Sebe, C. Djeraba, and R. Jain, “Content-based multimedia information retrieval: state of the art and challenges,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 2, no. 1, pp. 1–19, 2006. View at Google Scholar · View at Scopus
  6. Y. Rui, T. Huang, and S. Chang, “Image retrieval: current techniques, promising directions and open issues,” Journal of Visual Communication and Image Representation, vol. 10, no. 4, pp. 39–62, 1999. View at Google Scholar
  7. P. Clough, M. Grubinger, T. Deselaers, A. Hanbury, and H. Müller, “Overview of ImageCLEF 2006 Photographic retrieval and object annotation tasks,” in Proceedings of the 7th Workshop of the Cross-Language Evaluation Forum (CLEF '07), vol. 4730 of Lecture Notes in Computer Science, pp. 579–594, Springer, 2007.
  8. P. K. Atry, M. A. Hossain, A. E. Saddik, and M. S. Kankanhalli, “Multimodal fusion for multimedia analysis,” Multimedia Systems, vol. 16, no. 6, pp. 345–379, 2010. View at Google Scholar
  9. M. Broilo and F. G. B. De Natale, “A stochastic approach to image retrieval using relevance feedback and particle swarm optimization,” IEEE Transactions on Multimedia, vol. 12, no. 4, pp. 267–277, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Rui, T. Huang, M. Ortega, and S. Mehrotra, “Relevance feedback: a power tool for interactive content-based image retrieval,” IEEE Transactions on Circuits and Systems For Video Technology, vol. 8, no. 5, pp. 644–655, 1998. View at Google Scholar
  11. X. Zhou and T. Huang, “Relevance feedback in image retrieval: a comprehensive review,” Multimedia Systems, vol. 8, pp. 536–544, 2003. View at Google Scholar
  12. T. Deselaers, T. Gass, P. Dreuw, and H. Ney, “Jointly optimising relevance and diversity in image retrieval,” in Proceedings of the ACM International Conference on Image and Video Retrieval (CIVR '09), pp. 296–303, ACM Press, July 2009, paper 39. View at Publisher · View at Google Scholar · View at Scopus
  13. H. J. Escalante, C. Hernandez, E. Sucar, and M. Montes, “Late fusion of heterogeneous methods for multimedia image retrieval,” in Proceedings of the ACM Multimedia Information Retrieval Conference, pp. 172–179, ACM Press, Vancouver, Canada, 2008.
  14. A. Juàrez, M. Montes, L. Villaseñor, D. Pinto, and M. Pérez, “Selecting the N-top retrieval result lists for an effective data fusion,” in Proceedings of the 11th International Conference on Intelligent Text Processing and Computational Linguistics, vol. 6008 of Lecture Notes in Computer Science, pp. 580–589, Springer, 2010.
  15. Y. Chang, W. Lin, and H.-H. Chen, “Combining text and image queries at ImageCLEF 2005,” in Working Notes of the CLEF Workshop, Vienna, Austria, 2005.
  16. A. Marakakis, N. Galatsanos, A. Likas, and A. Stafylopatis, “Application of relevance feedback in content based image retrieval using gaussian mixture models,” in Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI '08), pp. 141–148, November 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. X. Tian, L. Yang, J. Wang, Y. Yang, X. Wu, and X. S. Hua, “Bayesian video search reranking,” in Proceedings of the 16th ACM International Conference on Multimedia, pp. 131–140, ACM Press, Vancouver, Canada, 2008.
  18. Y. Jing and S. Baluja, “PageRank for product image search,” in Proceedings of the International World Wide Web Conference Committee, pp. 307–315, ACM Press, Beijing, China, 2008.
  19. T. Yao, T. Mei, and C. W. Ngo, “Co-reranking by mutual reinforcement for image search,” in Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 34–41, ACM Press, Xian, China, 2010.
  20. J. Cui, F. Wen, and X. Tang, “Real time google and live image search re-ranking,” in Proceedings of the ACM Multimedia Information Retrieval Conference, pp. 729–732, ACM Press, Vancouver, Canada, 2008.
  21. W. Lin, R. Jin, and A. Hauptmann, “A web image retrieval re-ranking with relevance model,” in Proceedings of the IEEE International Conference on Web Intelligence, p. 242, 2003.
  22. H. Müller, P. Clough, T. Deselaers, and B. Caputo, ImageCLEF: Experimental Evaluation in Visual Information Retrieval, Springer Series on Information Retrieval, 2010.
  23. M. Grubinger, Analysis and evaluation of visual information systems performance [Ph.D. thesis], School of Computer Science and Mathematics, Faculty of Health, Engineering and Science, Victoria University, Melbourne, Australia, 2007.
  24. P. Clough, M. Grubinger, T. Deselaers, A. Hanbury, and H. Müller, “Overview of the ImageCLEF 2007 photographic retrieval task,” in Proceedings of the 8th Workshop of the Cross-Language Evaluation Forum (CLEF '08), vol. 5152 of Lecture Notes in Computer Science, pp. 433–444, Springer, 2008.
  25. T. Arni, M. Sanderson, P. Clough, and M. Grubinger, “Overview of the ImageCLEF 2007 photographic retrieval task,” in Evaluating Systems for Multilingual and Multimodal Information Access, vol. 5706 of Lecture Notes in Computer Science, pp. 500–511, Springer, 2009. View at Google Scholar
  26. R. O. Chàvez, M. Montes, and E. Sucar, “Using a markov random field for image re-ranking based on visual and textual features,” Computación y Sistemas, vol. 14, no. 4, pp. 393–404, 2011. View at Google Scholar
  27. R. O. Chàvez, M. Montes, and E. Sucar, “Image Re-ranking based on relevance feedback combining internal and external similarities,” in Proceedings of the 23rd International FLAIRS Conference, pp. 140–141, Daytona Beach, Fla, USA, 2010.
  28. I. J. Cox, M. L. Miller, T. P. Minka, T. V. Papathomas, and P. N. Yianilos, “The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments,” IEEE Transactions on Image Processing, vol. 9, no. 1, pp. 20–37, 2000. View at Google Scholar · View at Scopus
  29. C. Zhang, J. Y. Chai, and R. Jin, “User term feedback in interactive text-based image retrieval,” in Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 51–58, ACM Press, Salvador, Brazil, 2005.
  30. Z. H. Zhou, K. E. J. Chen, and H. B. Dai, “Enhancing relevance feedback in image retrieval using unlabeled data,” ACM Transactions on Information Systems, vol. 24, no. 2, pp. 219–244, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Tong and E. Chang, “Support vector machine active learning for image retrieval,” in Proceedings of the ninth ACM international conference on Multimedia, pp. 107–118, ACM Press, Ottawa, Canada, 2001.
  32. T. Deselaers, R. Paredes, E. Vidal, and H. Ney, “Learning weighted distances for relevance feedback in image retrieval,” in Proceedings of the 19th International Conference on Pattern Recognition (ICPR '08), pp. 1–4, Tampa, Fla, USA, December 2008. View at Scopus
  33. R. Yan, A. G. Hauptmann, and R. Jin, “Negative pseudo-relevance feedback in content-based video retrieval,” in Proceedings of the 11th ACM International Conference on Multimedia, pp. 343–346, ACM Press, Berkeley, Calif, USA, 2003.
  34. R. Yan, A. G. Hauptmann, and R. Jin, “Multimedia search with pseudo-relevance feedback,” in Proceedings of the International Conference on Image and Video Retrieval, ACM Press, Urbana, Ill, USA, 2003.
  35. H. Ma, J. Zhu, M. R. Lyu, and I. King, “Bridging the semantic gap between image contents and tags,” IEEE Transactions on Multimedia, vol. 12, no. 5, pp. 462–473, 2010. View at Google Scholar
  36. H. Tong, J. He, M. Li, W. Y. Ma, H. J. Zhang, and C. Zhang, “Manifoldranking-based keyword propagation for image retrieval,” EURASIP Journal on Applied Signal Processing, vol. 2006, Article ID 079412, 2006. View at Publisher · View at Google Scholar
  37. J. Ah-Pine, M. Bressan, S. Clinchant, G. Csurka, Y. Hoppenot, and J. M. Renders, “Crossing textual and visual content in different application scenarios,” Multimedia Tools and Applications, vol. 42, no. 1, pp. 31–56, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. K. Porkaew and K. Chakrabarti, “Query refinement for multimedia similarity retrieval in MARS,” in Proceedings of the 7th ACM International Conference on Multimedia, pp. 235–238, ACM Press, 1999.
  39. K. Porkaew, M. Ortega, and S. Mehrotra, “Query reformulation for content based multimedia retrieval in MARS,” in Proceedings of the 6th International Conference on Multimedia Computing and Systems (IEEE ICMCS '99), pp. 747–751, June 1999. View at Scopus
  40. G. Giacinto and F. Roli, “Nearest-prototype relevance feedback for content-based image retrieval,” in Proceedings of the 17th International Conference on Patternt Recognition, vol. 2, pp. 989–992, Washington, DC, USA, 2004.
  41. G. Giacinto and F. Roli, “Instance-based relevance feedback for image retrieval,” in Advances in Neural Information Processing Systems, vol. 17, pp. 489–496, MIT Press, 2005. View at Google Scholar
  42. G. Giacinto and F. Roli, “Instance-based relevance feedback in image retrieval using dissimilarity spaces,” in Case-Based Reason-Ing for Signals and Images, pp. 419–430, Springer, 2007. View at Google Scholar
  43. P. H. Gosselin and M. Cord, “Active learing techniques for user interactive systems: application to image retrieval,” in Proceedings of the Workshop Machine Learning Techniques for Processing Multimedia Content, Bonn, Germany, 2005.
  44. L. Setia, J. Ick, and H. Burkhardt, “SVM-based relevance feedback in image retrieval using invariant feature histograms,” in Proceedings of the IAPR Workshop on Machine Vision Applications, Tsukuba Science City, Japan, 2005.
  45. Y. Chen, X. Zhou, and T. Huang, “One-class SVM for learning in image retrieval,” in Proceedings of the International Conference on Image Processing, pp. 34–37, Thessaloniki, Greece, 2001.
  46. Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, “An efficient boosting algorithm for combining preferences,” Journal of Machine Learning Research, vol. 4, no. 6, pp. 933–969, 2004. View at Publisher · View at Google Scholar · View at Scopus
  47. V. Lavrenko and W. B. Croft, “Relevance-based language models,” in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 120–127, ACM Press, 2001.
  48. G. Winkler, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, Springer Series on Applications of Mathematics, Springer, 2006.
  49. S. Z. Li, Markov Random Field Modeling in Image Analysis, Springer, 2nd edition, 2001.
  50. S. Z. Li, “Markov random field models in computer vision,” in Proceedings of the European Conference on Computer Vision, vol. 801 of Lecture Notes in Computer Science, pp. 361–370, Springer, Stockholm, Sweden, 1994.
  51. K. Held, E. Kops, B. Krause, W. Wells III, R. Kikinis, and H. Mueller, “Markov random field segmentation of brain MR images,” IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp. 878–886, 1997. View at Publisher · View at Google Scholar
  52. S. Geman and D. Geman, “Stochastic relaxation, gibbs distributions, and the bayesian restoration of images,” in Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, pp. 564–584, 1987. View at Google Scholar
  53. P. Carbonetto, N. de Freitas, and K. Barnard, “A statistical model for general context object recognition,” in Proceedings of the 8th European Conference on Computer Vision, vol. 3021 of Lecture Notes in Computer Science, pp. 350–362, Springer, Prague, Czech Republic, 2004.
  54. C. Hernandez and L. E. Sucar, “Markov random fields and spatial information to improve automatic image annotation,” in Proceedings of the Pacic-Rim Symposium on Image and Video Technology, vol. 4872 of Lecture Notes in Computer Science, pp. 879–892, Springer, Santiago, Chile, 2007.
  55. H. J. Escalante, M. Montes, and L. E. Sucar, “Word Co-occurrence and markov random fields for improving automatic image annotation,” in Proceedings of the 18th British Machine Vision Conference, vol. 2, pp. 600–609, Warwick, UK, 2007.
  56. D. Metzler and B. Croft, “A markov random field model for term dependencies,” in Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 472–479, ACM Press, 2005.
  57. D. Metzler and W. B. Croft, “Latent concept expansion using Markov random fields,” in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '07), pp. 311–318, ACM Press, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  58. M. Lease, “An improved markov random field model for supporting verbose queries,” in Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '09), pp. 476–483, ACM Press, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  59. J. Besag, “On the statistical analysis of dirty pictures,” Jounal of the Royal Statistical Society B, vol. 48, pp. 259–302, 1986. View at Google Scholar
  60. S. Kirkpatrick, C. Gelatt, and M. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983. View at Publisher · View at Google Scholar
  61. Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222–1239, 2001. View at Publisher · View at Google Scholar · View at Scopus
  62. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar
  63. E. A. Fox and J. A. Shaw, “Combination of multiple searches,” in Proceedings of The 3rd Text REtrieval Conference (TREC '04), NIST Publication, 1994.
  64. H. J. Escalante, J. A. Gonzalez, C. Hernandez et al., “Annotation-based expansion and late fusion of mixed methods for multimedia image retrieval,” in Evaluating Systems for Multilingual and Multimodal Information Access, vol. 5706 of Lecture Notes in Computer Science, pp. 669–676, Springer, 2009. View at Google Scholar
  65. I. Mani, Automatic Summarization (Natural Language Processing), John Benjamins Publishing Co, 2001.
  66. M. D. Smucker, J. Allan, and B. Carterette, “Agreement among statistical significance tests for information retrieval evaluation at varying sample sizes,” in Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '09), pp. 630–631, ACM Press, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  67. K. G. Kanji, 100 Statistical Tests / Gopal K. Kanji, Sage, London, UK, 1993.
  68. H. J. Escalante, C. A. Hernández, J. A. Gonzalez et al., “The segmented and annotated IAPR TC-12 benchmark,” Computer Vision and Image Understanding, vol. 114, no. 4, pp. 419–428, 2010. View at Publisher · View at Google Scholar · View at Scopus
  69. C. Snoek, M. Worring, A. Smeulders, and W. M. Arnold, “Early versus late fusion in semantic video analysis,” in Proceedings of the 13th Annual ACM International Conference on Multimedia (MULTIMEDIA '05), pp. 399–402, ACM Press, New York, NY, USA, 2005.