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Efficient Content-Based Image Retrieval through Metric Histograms

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Abstract

This paper presents a new and efficient method for content-based image retrieval employing the color distribution of images. This new method, called metric histogram, takes advantage of the correlation among adjacent bins of histograms, reducing the dimensionality of the feature vectors extracted from images, leading to faster and more flexible indexing and retrieval processes. The proposed technique works on each image independently from the others in the dataset, therefore there is no pre-defined number of color regions in the resulting histogram. Thus, it is not possible to use traditional comparison algorithms such as Euclidean or Manhattan distances. To allow the comparison of images through the new feature vectors given by metric histograms, a new metric distance function MHD( ) is also proposed. This paper shows the improvements in timing and retrieval discrimination obtained using metric histograms over traditional ones, even when using images with different spatial resolution or thumbnails. The experimental evaluation of the new method, for answering similarity queries over two representative image databases, shows that the metric histograms surpass the retrieval ability of traditional histograms because they are invariant on geometrical and brightness image transformations, and answer the queries up to 10 times faster than the traditional ones.

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References

  1. M. Albanesi, S. Bandelli, and M. Ferretti, “Quantitative assessment of qualitative color perception in image database retrieval,” in 11th International Conference on Image Analysis and Processing, Thessaloniki, Greece, 2001, pp. 410-415.

  2. E. Albuz, E. Kocalar, and A. A. Khokhar, “Scalable color image indexing and retrieval using vector wavelets,” IEEE Transactions on Knowledge and Data Engineering 13(5), 2001, 851-861.

    Google Scholar 

  3. D. Androutsos, K. N. Plataniotis, and A. N. Venetsanopoulos, “A novel vector-based approach to color image retrieval using a vector angular-based distance measure,” Computer Vision and Image Understanding 75(1/2), 1999, 46-58.

    Google Scholar 

  4. M. R. B. Araujo, C. Traina Jr., A. J. M. Traina, J. M. Bueno, and H. L. Razente, “Extending relational databases to support content-based retrieval of medical images,” in IEEE International Conference on Computer Based Medical Systems - CBMS, Maribor, Slovenia, 2002, pp. 303-308.

  5. Y. A. Aslandogan and C. T. Yu, “Techniques and systems for image and video retrieval,” IEEE Transactions on Knowledge and Data Engineering 11(1), 1999, 56-63.

    Google Scholar 

  6. R. Baeza-Yates and B. A. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, Wokingham, UK, 1999.

    Google Scholar 

  7. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, “The R*-tree: An efficient and robust access method for points and rectangles,” in ACM International Conference on Data Management (SIGMOD), 1990, pp. 322-331.

  8. S. Berchtold, D. A. Keim, and H.-P. Kriegel, “The X-tree: An index structure for high-dimensional data,” in International Conference on Very Large Databases (VLDB), Bombay, India, 1996, pp. 28-39.

  9. A. Berman and L. G. Shapiro, “Selecting good keys for triangle-inequality-based pruning algorithms,” in International Workshop on Content-Based Access of Image and Video Databases (CAIVD'98), Bombay, India, 1998, pp. 12-19.

  10. T. Bozkaya and Z. M. Zsoyoglu, “Indexing large metric spaces for similarity search queries,” ACM Transactions on Database Systems (TODS) 24(3), 1999, 361-404.

    Google Scholar 

  11. L. G. Brown, “A survey of image registration techniques,” ACM Computing Surveys 24(4), 1992, 325-376.

    Google Scholar 

  12. R. Brunelli and O. Mich, “On the use of histograms for image retrieval,” in IEEE International Conference on Multimedia Computing and Systems (ICMCS), Vol. 2, Florence, Italy, 1999, pp. 143-147.

    Google Scholar 

  13. J. M. Bueno, F. Chino, A. J. M. Traina, C. Traina Jr., and P. M. d. A. Marques, “How to add contentbased image retrieval capability in a PACS,” in IEEE International Conference on Computer Based Medical Systems - CBMS, Maribor, Slovenia, 2002, pp. 321-326.

  14. W. A. Burkhard and R. M. Keller, “Some approaches to best-match file searching,” Communications of the ACM 16(4), 1973, 230-236.

    Google Scholar 

  15. K. Chakrabarti and S. Mehrotra, “The hybrid tree: An index structure for high dimensional feature spaces,” in International Conference on Data Engineering (ICDE), Sydney, Australia, 1999, pp. 440-447.

  16. E. Chvez, G. Navarro, R. A. Baeza-Yates, and J. L. Marroqun, “Searching in metric spaces,” ACM Computing Surveys 33(3), 2001, 273-321.

    Google Scholar 

  17. P. Ciaccia, M. Patella, and P. Zezula, “M-tree: An efficient access method for similarity search in metric spaces,” in International Conference on Very Large Databases (VLDB), Athens, Greece, ed.M. Jarke, 1997, pp. 426-435.

  18. D. Comer, “The ubiquitous B-tree,” ACM Computing Surveys 11(2), 1979, 121-137.

    Google Scholar 

  19. C. Esperanca and H. Samet, “A differential code for shape representation in image database application,” in International Conference on Image Processing, 1997.

  20. C. Faloutsos, Searching Multimedia Databases by Content, Kluwer Academic, Boston, MA, 1996.

    Google Scholar 

  21. M. Flickner et al., “Query by image and video content: The QBIC system,” IEEE Computer 28(9), 1995, 23-32.

    Google Scholar 

  22. V. Gaede and O. Gnther, “Multidimensional access methods,” ACM Computing Surveys 30(2), 1998, 170- 231.

    Google Scholar 

  23. G. Gagaudakis and P. L. Rosin, “Shape measures for image retrieval,” in International Conference on Image Processing, Thessaloniki, Greece, 2001, pp. 757-760.

  24. H. Garcia-Molina, J. D. Ulhman, and J. Widow, Database System Implementation, Prentice-Hall, 2000.

  25. G. L. Gimel'farb and A. K. Jain, “On retrieving textured images from an image database,” Pattern Recognition 29(9), 1996, 1,461-1,483.

    Google Scholar 

  26. U. Gntzer, W.-T. Balke, and W. Kiessling, “Optimizing multi-feature queries for image databases,” in International Conference on Very Large Databases (VLDB), Cairo, Egypt, 2000, pp. 419-428.

  27. A. Gupta and S. Santini, “Toward feature algebras in visual databases: The case for a histogram algebra,” in Fifth Working Conference on Visual Database Systems (VDB5), Fukuoka, Japan, Vol. 168, eds. H. Arisawa and T. Catarci, 2000.

  28. A. Guttman, “R-tree: A dynamic index structure for spatial searching,” in ACM International Conference on Data Management (SIGMOD), Boston, MA, 1984, pp. 47-57.

  29. J. Hafner, H. Sawhney, W. Equitz, M. Flickner, and W. Niblack, “Efficient color histogram indexing for quadratic form distance function,” IEEE Transactions on Patterns Analysis and Machine Intelligence 17(7), 1995, 729-736.

    Google Scholar 

  30. I. Kamel and C. Faloutsos, “Hilbert R-tree: An improved R-tree using fractals,” in 20th International Conference on Very Large Data Bases (VLDB), Santiago del Chile, Chile, eds. J. B. Bocca, M. Jarke, and C. Zaniolo, 1994, pp. 500-509.

  31. K. V. R. Kanth, D. Agrawal, A. El Abbadi, and A. K. Singh, “Dimensionality reduction for similarity searching in dynamic databases,” Computer Vision and Image Understanding 75(1/2), 1999, 59-72.

    Google Scholar 

  32. B. Ko, H.-S. Lee, and H. Byun, “Image retrieval using flexible image subblocks,” in ACM Symposium on Applied Computing, Vol. 2, 2000, pp. 574-578.

    Google Scholar 

  33. F. Korn, B.-U. Pagel, and C. Faloutsos, “On the ‘Dimensionality Curse’ and the ‘self-Similarity Blessing’,” IEEE Transactions on Knowledge and Data Engineering 13(1), 2001, 96-111.

    Google Scholar 

  34. W.-J. Kuo and R.-F. Chang, “Approximating the statistical distribution of color histogram for contentbased image retrieval,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'00, Vol. 4, 2000, pp. 2007-2010.

    Google Scholar 

  35. J.-H. Lim, J. K. Wu, S. Singh, and D. Narasimhalu, “Learning similarity matching in multimedia contentbased retrieval,” IEEE Transactions on Knowledge and Data Engineering 13(5), 2001, 846-850.

    Google Scholar 

  36. K.-I. D. Lin, H. V. Jagadish, and C. Faloutsos, “The TV-tree: An index structure for high-dimensional data,” VLDB Journal 3(4), 1994, 517-542.

    Google Scholar 

  37. M. K. Mandal, S. Panchanathan, and T. Aboulnasr, “Fast wavelet histogram techniques for image indexing,” in IEEE Workshop on Content-Based Access of Image and Video Libraries, Santa Barbara, CA, 1998.

  38. V. E. Ogle and M. Stonebraker, “Chabot: Retrieval from relational databases of images,” IEEE Computer 28(9), 1995, 40-48.

    Google Scholar 

  39. M. Ortega-Binderberger, S. Mehrotra, K. Chakrabarti, and K. Porkaew, “WebMARS: A multimedia search engine for full document retrieval and cross media browsing,” in Multimedia Information Systems Workshop 2000 (MIS2000), Chicago, IL, 2000.

  40. G. Pass, R. Zabih, and J. Miller, “Comparing images using color coherence vector,” in ACM Multimedia, Boston, MA, 1996, pp. 65-73.

  41. A. Pentland, R. Picard, and S. Sclaroff, “Content-based manipulation of image databases,” International Journal of Computer Vision 18(3), 1996, 233-254.

    Google Scholar 

  42. J. T. Robinson, “The K-D-B-tree: A search structure for large multidimensional dynamic indexes,” in: ACM International Conference on Management of Data - SIGMOD, Ed. Y. E. Lien, 1981, pp. 10-18.

  43. H. Samet, “Spatial data structures in modern database systems: The object model, interoperability, and beyond,” Addison-Wesley/ACM Press, 1995, pp. 361-385.

  44. R. F. Santos, A. J. M. Traina, C. Traina Jr., and C. Faloutsos, “Similarity search without tears: The OMNI family of all-purpose access methods,” in International Conference on Data Engineering (ICDE), Heidelberg, Germany, 2001, pp. 623-630.

  45. T. K. Sellis, N. Roussopoulos, and C. Faloutsos, “The R+-tree: A dynamic index for multi-dimensional objects,” in International Conference on Very Large Databases (VLDB), Brighton, England, 1987, pp. 507-518.

  46. C.-R. Shyu, C. E. Brodley, A. C. Kak, A. Kosaka, A. M. Aisen, and L. S. Broderick, “ASSERT: A physicianin-the-loop content-based retrieval system for HRGT image databases,” Computer Vision and Image Understanding 75(1/2), 1999, 111-132.

    Google Scholar 

  47. E. L. Siegel, “Current state of the art and future trends,” in Filmless Radiology, eds. E. L. Siegel and R. M. Kolodner, Springer Verlag, New York, 1999, pp. 3-20.

    Google Scholar 

  48. E. L. Siegel and R. M. Kolodner, Filmless Radiology, Springer Verlag, New York, 1999.

    Google Scholar 

  49. 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 Patterns Analysis and Machine Intelligence 22(12), 2000.

  50. J. R. Smith and S.-F. Chang, “VisualSEEk: a fully automated content-based image query system,” in ACM Multimedia'96, Boston, MA, 1996, pp. 87-98.

  51. F. Tomita and T. Saburo, Computer Analysis of Visual Textures, Kluwer, 1990.

  52. A. J. M. Traina, C. Traina Jr., J. M. Bueno, and P. M. d. A. Marques, “The metric histogram: A new and efficient approach for content-based image retrieval,” in Sixth IFIP Working Conference on Visual Database Systems, Brisbane, Australia, 2002, pp. 297-311.

  53. C. Traina Jr., A. J. M. Traina, C. Faloutsos, and B. Seeger, “Fast indexing and visualization of metric datasets using slim-trees,” IEEE Transactions on Knowledge and Data Engineering 14(2), 2002, 244-260.

    Google Scholar 

  54. C. Traina Jr., A. J. M. Traina, B. Seeger, and C. Faloutsos, “Slim-trees: High performance metric trees minimizing overlap between nodes,” in International Conference on Extending Database Technology, eds. C. Zaniolo, P. C. Lockemann, M. H. Scholl, and T. Grust, Lecture Notes in Computer Science, Vol. 1777, Konstanz, Germany, 2000, pp. 51-65.

  55. T. Tuytelaars and L. J. V. Gool, “Content-based image retrieval based on local affinely invariant regions,” in Third International Conference on Visual Information and Information Systems - VISUAL'99, eds. D. P. Huijsmans and A. W. M. Smeulders, Lecture Notes in Computer Science, Vol. 1614, Amsterdam, The Netherlands, 1999, pp. 493-500.

  56. A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H.-J. Zhang, “Image classification for content-based indexing,” IEEE Transactions on Image Processing 10(1), 2001, 117-130.

    Google Scholar 

  57. H. Yamamoto, H. Iwasa, N. Yokoya, and H. Takemura, “Content-based similarity retrieval of images based on spatial color distributions,” in 10th International Conference on Image Analysis and Processing, 1999, pp. 951-956.

  58. P. Zhou, J. F. Feng, and Q. Y. Shi, “Texture feature based on local Fourier transform,” in International Conference on Image Processing, Vol. 2, Thessaloniki, Greece, 2001, pp. 610-613.

    Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, University of Sao Paulo at Sao Carlos, Brazil

    A. J. M. Traina, C. Traina, J. M. Bueno & F. J. T. Chino

  2. Science of Image and Medical Physics Center, Medical School of Ribeirao Preto, University of Sao Paulo at Ribeirao Preto, Brazil

    P. Azevedo-Marques

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Traina, A.J.M., Traina, C., Bueno, J.M. et al. Efficient Content-Based Image Retrieval through Metric Histograms. World Wide Web 6, 157–185 (2003). https://doi.org/10.1023/A:1023670521530

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