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Accelerating of Image Retrieval in CBIR System with Relevance Feedback

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Abstract

Content-based image retrieval (CBIR) system with relevance feedback, which uses the algorithm for feature-vector (FV) dimension reduction, is described. Feature-vector reduction (FVR) exploits the clustering of FV components for a given query. Clustering is based on the comparison of magnitudes of FV components of a query. Instead of all FV components describing color, line directions, and texture, only their representative members describing FV clusters are used for retrieval. In this way, the "curse of dimensionality" is bypassed since redundant components of a query FV are rejected. It was shown that about one tenth of total FV components (i.e., the reduction of 90%) is sufficient for retrieval, without significant degradation of accuracy. Consequently, the retrieving process is accelerated. Moreover, even better balancing between color and line/texture features is obtained. The efficiency of FVR CBIR system was tested over TRECVid 2006 and Corel 60 K datasets.

References

  1. 1.

    Feng D, Siu WC, Zhang HJ (Eds): Multimedia Information Retrieval and Management. Springer, New York, NY, USA; 2003.

  2. 2.

    Chang N-S, Fu K-S: Query by pictorial example. IEEE Transactions on Software Engineering 1980,6(6):519-524.

  3. 3.

    Chang S-K, Kunii TL: Pictorial data-base systems. IEEE Computer Magazine 1981,14(11):13-21.

  4. 4.

    Swain MJ, Ballard DH: Color indexing. International Journal of Computer Vision 1991,7(1):11-32. 10.1007/BF00130487

  5. 5.

    Niblack CW, Barber R, Equitz W, et al.: QBIC project: querying images by content, using color, texture, and shape. Storage and Retrieval for Image and Video Databases, February 1993, San Jose, Calif, USA, Proceedings of SPIE 1908: 173–187.

  6. 6.

    Tamura H, Mori S, Yamawaki T: Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics 1978,8(6):460-473.

  7. 7.

    Manjunath BS, Ma WY: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996,18(8):837-842. 10.1109/34.531803

  8. 8.

    Jain AK, Vailaya A: Shape-based retrieval: a case study with trademark image databases. Pattern Recognition 1998,31(9):1369-1390. 10.1016/S0031-3203(97)00131-3

  9. 9.

    Pentland AP, Picard RW, Scarloff S: Photobook: tools for content-based manipulation of image databases. Storage and Retrieval for Image and Video Databases II, February 1994, San Jose, Calif, USA, Proceedings of SPIE 2185: 34–47.

  10. 10.

    Flickner M, Sawhney H, Niblack W, et al.: Query by image and video content: the QBIC system. Computer 1995,28(9):23-32. 10.1109/2.410146

  11. 11.

    Bach JR, Fuller C, Gupta A, et al.: Virage image search engine: an open framework for image management. Storage and Retrieval for Still Image and Video Databases IV, February 1996, San Jose, Calif, USA, Proceedings of SPIE 2670: 76–87.

  12. 12.

    Ma WY, Manjunath BS: NeTra: a toolbox for navigating large image databases. Proceedings of IEEE International Conference on Image Processing (ICIP '97), October 1997, Santa Barbara, Calif, USA 1: 568–571.

  13. 13.

    Rui Y, Huang TS, Mehrotra S: Content-based image retrieval with relevance feedback in MARS. Proceedings of IEEE International Conference on Image Processing (ICIP '97), October 1997, Santa Barbara, Calif, USA 2: 815–818.

  14. 14.

    Rui Y, Huang TS, Ortega M, Mehrotra S: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 1998,8(5):644-655. 10.1109/76.718510

  15. 15.

    Mitchell TM: Machine Learning. McGraw-Hill, New York, NY, USA; 1997.

  16. 16.

    Karhunen J, Joutsensalo J: Representation and separation of signals using nonlinear PCA type learning. Neural Networks 1994,7(1):113-127. 10.1016/0893-6080(94)90060-4

  17. 17.

    Haykin S: Neural Networks: A Comprehensive Foundation. John Wiley & Sons, New York, NY, USA; 1999.

  18. 18.

    Peng J, Bhanu B, Qing S: Probabilistic feature relevance learning for content-based image retrieval. Computer Vision and Image Understanding 1999,75(1):150-164. 10.1006/cviu.1999.0770

  19. 19.

    Aggarwal G, Ashwin TV, Ghosal S: An image retrieval system with automatic query modification. IEEE Transactions on Multimedia 2002,4(2):201-214. 10.1109/TMM.2002.1017734

  20. 20.

    Lu G: Techniques and data structures for efficient multimedia retrieval based on similarity. IEEE Transactions on Multimedia 2002,4(3):372-384. 10.1109/TMM.2002.802831

  21. 21.

    Muneesawang P, Guan L: An interactive approach for CBIR using a network of radial basis functions. IEEE Transactions on Multimedia 2004,6(5):703-716. 10.1109/TMM.2004.834866

  22. 22.

    Lee K-M, Street WN: Cluster-driven refinement for content-based digital image retrieval. IEEE Transactions on Multimedia 2004,6(6):817-827. 10.1109/TMM.2004.837235

  23. 23.

    Ko B, Byun H: FRIP: a region-based image retrieval tool using automatic image segmentation and stepwise Boolean AND matching. IEEE Transactions on Multimedia 2005,7(1):105-113.

  24. 24.

    Calic J, Campbell N, Calway A, et al.: Towards intelligent content based retrieval of wildlife videos. In Proceedings of the 6th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '05), April 2005, Montreux, Switzerland. EFPL;

  25. 25.

    Čabarkapa S, Kojić N, Radosavljević V, Zajić G, Reljin B: Adaptive content-based image retrieval with relevance feedback. Proceedings of the International Conference on Computer as a Tool (EUROCON '05), November 2005, Belgrade, Serbia 1: 147–150.

  26. 26.

    Radosavljević V, Kojić N, Čabarkapa S, Zajić G, Reljin I, Reljin B: An image retrieval system with user's relevance feedback. Proceedings of the 7th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '06), April 2006, Seоul, Korea 9–12.

  27. 27.

    Kruskal JB, Wish M: Multidimensional Scaling. Sage, Beverly Hills, Calif, USA; 1977.

  28. 28.

    Jolliffe IT: Principal Component Analysis. 2nd edition. Springer, New York, NY, USA; 2002.

  29. 29.

    Diamantaras KI, Kung SY: Principal Component Neural Networks. John Wiley & Sons, New York, NY, USA; 1996.

  30. 30.

    Turk M, Pentland AP: Eigenfaces for recognition. Journal of Cognitive Neuroscience 1991,3(1):71-86. 10.1162/jocn.1991.3.1.71

  31. 31.

    Serre T, Heisele B, Mukherjee S, Poggio T: Feature Selection for Face Detection. MIT A.I. Memo no. 1697, September, 2000

  32. 32.

    Fisher RA: The use of multiple measurements in taxonomic problems. Annals of Eugenics 1936, 7: 179–188. 10.1111/j.1469-1809.1936.tb02137.x

  33. 33.

    Fisher RA: The statistical utilization of multiple measurements. Annals of Eugenics 1938, 8: 376–386. 10.1111/j.1469-1809.1938.tb02189.x

  34. 34.

    Belhumeur PN, Hespanha JP, Kriegman DJ: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):711-720. 10.1109/34.598228

  35. 35.

    Etemad K, Chellappa R: Discriminant analysis for recognition of human face images. Journal of the Optical Society of America A 1997,14(8):1724-1733. 10.1364/JOSAA.14.001724

  36. 36.

    Wu Y, Tian Q, Huang TS: Discriminant-EM algorithm with application to image retrieval. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '00), June 2000, Hilton Head Island, SC, USA 1: 222–227.

  37. 37.

    Fukunaga K: Statistical Pattern Recognition. 2nd edition. Academic Press, New York, NY, USA; 1990.

  38. 38.

    Yu H, Yang J: A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recognition 2001,34(10):2067-2070. 10.1016/S0031-3203(00)00162-X

  39. 39.

    Zhou XS, Huang TS: Small sample learning during multimedia retrieval using BiasMap. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 11–17.

  40. 40.

    Tao D, Tang X, Li X, Rui Y: Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Transactions on Multimedia 2006,8(4):716-727.

  41. 41.

    Shen J, Shepherd J, Ngu AHH: Towards effective content-based music retrieval with multiple acoustic feature combination. IEEE Transactions on Multimedia 2006,8(6):1179-1189.

  42. 42.

    Vapnik VN: The Nature of Statistical Learning Theory. Springer, New York, NY, USA; 1995.

  43. 43.

    Zhang L, Lin F, Zhang B: Support vector machine learning for image retrieval. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 2: 721–724.

  44. 44.

    Guo G-D, Jain AK, Ma W-Y, Zhang H-J: Learning similarity measure for natural image retrieval with relevance feedback. IEEE Transactions on Neural Networks 2002,13(4):811-820. 10.1109/TNN.2002.1021882

  45. 45.

    Corel Gallery Magic 65000 (1999), https://doi.org/www.corel.com/

  46. 46.

    https://doi.org/vismod.media.mit.edu/pub/VisTex/

  47. 47.

    Howarth P, Rüger S: Trading precision for speed: localised similarity functions. In Proceedings of the 4th International Conference on Image and Video Retrieval (CIVR '05), July 2005, Singapore, Lecture Notes in Computer Science. Volume 3568. Springer; 415–424.

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Correspondence to Goran Zajić.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zajić, G., Kojić, N., Radosavljević, V. et al. Accelerating of Image Retrieval in CBIR System with Relevance Feedback. EURASIP J. Adv. Signal Process. 2007, 062678 (2007) doi:10.1155/2007/62678

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Keywords

  • Color
  • Information Technology
  • Quantum Information
  • Image Retrieval
  • Dimension Reduction