Bibliography

  • Maytham H. Safar
  • Cyrus Shahabi
Part of the Multimedia Systems and Applications book series (MMSA, volume 23)

Keywords

Topo Univer Anil 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    R. Agrawal, C. Faloutsos, and A. Swami Efficient Similarity Search In Sequence Databases FODO,1993. Google Scholar
  2. [2]
    R. Agrawal, K. Lin, H. Sawhney, and K. Shim Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time--Series Databases Proceedings of the 21st VLDB Conference Zurich, Switzerland, 1995.Google Scholar
  3. [3]
    H. Alt, Blömer J. Resemblance and Symmetries of Geometric Patterns Data Structures and Efficient Algorithms, in: LNCS, Vol. 594, pp.1–24, Springer 1992.CrossRefGoogle Scholar
  4. [4]
    G.N. Bebis, and G.M. Papadourakis Object recognition using invariant object boundary representations and neural network models Pattern Recognition, Vol. 25, pp. 25–44, 1992.CrossRefGoogle Scholar
  5. [5]
    R. Beckmann and H.P. Kriegel The R* - tree: An Efficient and Robust Access Method for Points and Rectangles Proceedings ACM SIGMOD International Conference on Management of Data, Atlantic City, NJ, pp. 322–331, 1990Google Scholar
  6. [6]
    J.L. Bentley, and J.H. Friedman Data Structures for Range Searching. ACM Computing Surveys, 11(4), p. 397–409, December 1979.CrossRefGoogle Scholar
  7. [7]
    S. Berchtold, D. Keim, and H.P. Kriegel The X-tree: An Index Structure for High-Dimensional Data Proceedings of the 22nd VLDB Conference Mumbai, India, 1996.Google Scholar
  8. [8]
    S. Berchtold, and H.P. Kriegel S3: Similarity Search in CAD Database Systems SIGMOD, 1997.Google Scholar
  9. [9]
    S. Berchtold, D. Keim, and H.P. Kriegel Using Extended Feature Objects for Partial Similarity retrieval VLDB,1997. Google Scholar
  10. [10]
    S. Berchtold, and D. Keim Section Coding; A Similarity Search Technique for the Car Manufacturing Industry IDAT, 1998.Google Scholar
  11. [11]
    T. Bozkaya, and M. Ozsoyoglu Distance-Based Indexing for High-Dimensional Metric Spaces. Proceedings of SIGMOD International Conference on Management of Data, p. 357–368, 1997.Google Scholar
  12. [12]
    T. Brinkhoff, H.P. Kriegel, and R. Schneider Comparison of Approximations of Complex Objects Used for Approximation-based Query Processing in Spatial Database Systems In the Proceedings of the 9th International Conference on Data Engineering, ICDE, 1993.Google Scholar
  13. [13]
    P. Bruce Berra, F. Golshani, R. Mehrotra, and O. R. Sheng Guest Editors’ Introduction Multimedia Information Systems IEEE Transactions on Knowledge and data Engineering, Vol 5, No. 4, 1993.Google Scholar
  14. [14]
    A. Califano, and R. Mohan Multidimensional Indexing for Recognizing Visual Shapes IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 16, NO. 4, April 1994.Google Scholar
  15. [15]
    C. Chung, S. Lee, S. Chun, D. Kim, and J. Lee Similarity Search for Multidimensional Data Sequences In the Proceedings of the 16th International Conference on Data Engineering (ICDE), San Diego, CA, USA, February 29 - March 3, 2000.Google Scholar
  16. [16]
    P. Ciaccia, M. Patella, and P. Zezula M-Tree: An Efficient Access Method for Similarity Search in Metric Spaces. Proceedings of Very Large Data Bases Conference, 1997.Google Scholar
  17. [17]
    J.P. Eakins Retrieval of trade mark images by shape feature Proceedings First International Conference on Electronic Library and Visual Information System Research, de Montfort University, pp. 101–109, 1994.Google Scholar
  18. [18]
    J.P. Eakins, K. Sheilds and J.M. Boardman ARTISAN- a shape retrieval system based on boundary family indexing Storage and Retrieval for Image and Video Databases IV, (Sethi, I K and Jain, RC, eds), Proc SPIE 2670, pp. 17–28, 1996.Google Scholar
  19. [19]
    M.J. Egenhofer Reasoning about Binary Topological Relations. In the Proceedings of the Second Symposium on the Design and Implementation of Large Spatial Databases, Springer-Verlag LNCS 1991.Google Scholar
  20. [20]
    M.J. Egenhofer On the Robustness of Qualitative Distance- and Direction-Reasoning In the Proceedings of Auto-Carto 12, in Charlotte, North Carolina, pp. 301–310, 1995.Google Scholar
  21. [21]
    M.J. Egenhofer The Direction-Relation Matrix: A Representation for Direction Relations between Extended Spatial Objects http://www.spatialjnaine.edu/max/max.html 1997.Google Scholar
  22. [22]
    C. Faloutsos, and S. Roseman Fractals for Secondary Key Retrieval. Technical Report UMIACS-TR-89-47, CS-TR-2242, University of Maryland, College Park, Maryland, May 1989.Google Scholar
  23. [23]
    C. Faloutsos, M. Ranganathan, and Y. Manolopoulos Fast Subsequence Matching in Time-Series Databases SIGMOD.1994.Google Scholar
  24. [24]
    A.U. Frank Qualitative Spatial Reasoning with Cardinal Directions Proceedings of the Seventh Austrian Conference on Artificial Intelligence, Wien, Springer, Berlin, pp. 157–167, 1991.Google Scholar
  25. [25]
    A.U. Frank Qualitative Spatial Reasoning about Distances and Directions in Geographic Space Journal of Visual Languages and Computing,3 pp. 343–371, 1992.CrossRefGoogle Scholar
  26. [26]
    M. Freeston The BANG file: A new kind of grid file. Proceedings of ACM SIGMOD International Conference on Management of Data, San Francisco, CA, p. 260–269, 1987.Google Scholar
  27. [27]
    C. Freksa Using Orientation Information for Qualitative Spatial Reasoning In AM. Frank, I. Campari, U. Formentini (eds.) Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, LNCS 639, Springer-Verlag Berlin, 1992.CrossRefGoogle Scholar
  28. [28]
    J. Gary, and R. Mehrotra Feature-Based Retrieval of Similar Shapes International Conference on Data Engineering 1993.Google Scholar
  29. [29]
    J. Gary, and R. Mehrotra Similar-Shape Retrieval In Shape Data Management IEEE, Computer September 1995.Google Scholar
  30. [30]
    S. Ghandeharizadeh Stream-Based Versus Structured Video Objects: Issues, Solutions, and Challenges In S. Jajodia and V. Subrahmanian, eds, Multimedia DB Systems: Issues and Res. Direct., Springer-Verlag, 1995.Google Scholar
  31. [31]
    R.C. Gonzalez, and P. Wintz Digital Image Processing 2nd edition Addison-Wesley, Reading, Mass. 1987.Google Scholar
  32. [32]
    G.H. Granlund Fourier preprocessing for hand print character recognition IEEE Transactions on Computers, C-21, pp. 195–201, Feb. 1972.MathSciNetCrossRefGoogle Scholar
  33. [33]
    D. Greene An Implementation and Performance Analysis of Spatial Data Access Methods In the Proceedings of the 5th International Conference on Data Engineering, ICDE, 1989.Google Scholar
  34. [34]
    W. Grosky, P. Neo, and R. Mehrotra A Pictorial Index Mechanism fir Model-Based Matching International Conference on Data Engineering, 1989.Google Scholar
  35. [35]
    W. Grosky, and R. Mehrotra Index-Based Object Recognition in Pictorial Data Management Computer Vision, Graphics, and Image Processing Vol. 52,p.416–436, 1990.CrossRefGoogle Scholar
  36. [36]
    L. Guibas and J. Stolfi Primitives for the Manipulation of General Subdivisions and the Computation of Voronoi Diagrams ACM Transactions on Graphics, 4:74–123, April, 1986.CrossRefGoogle Scholar
  37. [37]
    O. Gunther The cell tree: an index for geometric data. Memorandum No. UCB/ERL M86/98, University of California, Berkeley, December 1986.Google Scholar
  38. [38]
    L. Guojun An approach to image retrieval based on shape Journal of Information Science, 23 (2), pp. 119–127, 1997.CrossRefGoogle Scholar
  39. [39]
    A. Guttman R-trees: A Dynamic Index Structure for Spatial Searching Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 47–57, 1984.Google Scholar
  40. [40]
    R.H. Guting An Introduction to Spatial Database Systems Invited Contribution to a Special Issue on Spatial Database Systems of the VLDB Journal, Vol. 3, No. 4, October 1994.Google Scholar
  41. [41]
    Pitas, Ioannis Digital Image Processing Algorithms Prentice Hall Englewood Cliffs, N.J. 1993.Google Scholar
  42. [42]
    Jagadish H. V. A Retrieval Technique for Similarity Shapes Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 208–217, 1991.Google Scholar
  43. [43]
    N. Katayama, and S. Satoh The SR-Tree: An Index Structure for High-Dimensional Nearest Neighbor Queries. Proceedings of SIGMOD International Conference on Management of Data, p. 369–380, 1997.Google Scholar
  44. [44]
    H. Kauppinen, T. Seppanen, M. Pietikainen An experimental comparison of autoregressive and Fourier-based descriptors in 2D shape classification IEEE Transaction on Pattern Analysis and Machine Intelligence, 17(2):201–207, 1995.CrossRefGoogle Scholar
  45. [45]
    W. Kim, and R.H. Park Contour coding based on the decomposition of line segments Pattern Recognition Letters, 11, 1999.Google Scholar
  46. [46]
    F. Korn, N. Sidiropoulos, C. Faloutsos, E. Siegel, and Z. Protopapas Fast Nearest Neighbor Search in Medical Image Databases Proceedings of 22nd VLDB Conference, pp.215–226, Mumbai, India, 1996.Google Scholar
  47. [47]
    R. Kurniawati, J.S. Jin, and J.A. Shepherd The SS + - tree: An improved index structure for similarity searches in a high-dimensional feature space. Proceedings of International Conference on storage and retrieval for image and video databases (SPIE), 1997.Google Scholar
  48. [48]
    C.S. Lin and C.L. Hwang New forms of shape invariants from elliptic Fourier descriptors Pattern Recognition, 20(5), pp. 535–545, 1987.CrossRefGoogle Scholar
  49. [49]
    D.B. Lomet, and B. Salzberg The hb—tree: a multi-attribute indexing method with good guaranteed performance. Proceedings of ACM TODS, 15(4), p. 625–658, December 1990.CrossRefGoogle Scholar
  50. [50]
    G. Lu, and A. Sajjanhar Region-based shape representation and similarity measure suitable for content-based image retrieval Multimedia Systems 7, Springer-Verlag, pp. 165–174, 1999(2).Google Scholar
  51. [51]
    N. Megiddo Linear-Time Algorithms for Linear Programming in R 3 and Related Problems SIAM Journal of Computation, 12, 108–116, 1984.Google Scholar
  52. [52]
    R. Mehrotra, and J.E. Gary Image retrieval using color and shape Second Asian Conference on Computer Vision, 5–8 December, Singapore. Springer, Berlin, pp. 529–533, 1995.Google Scholar
  53. [53]
    B.M. Mehtre, M.S. Kankanhalli, W.F. Lee Shape Measures for Content Based Image Retrieval: A Comparison Information Processing and Management, Vol.33, No.3, pp. 319–337, 1997.CrossRefGoogle Scholar
  54. [54]
    F. Mokhtarian, S. Abbasi, and J. Kitter Efficient and Robust Retrieval by Shape Content through Curvature Scale Space Proceedings of International Workshop on Image Database and Multimedia Search, pp. 35–42, Amesterdam, Netherlands, 1996.Google Scholar
  55. [55]
    W. Niblak, R. Barder, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Yaubin QBIC Project: Querying images by content using color, texture, and shape. Proceedings of SPIE Storage and Retrieval for Image and Video Databases, vol. 1908, p. 173–181, 1993.Google Scholar
  56. [56]
    J. Nievergelt, H. Hinterberger, and K.C. Sevcik The Grid File: An Adaptable Symmetric Multikey File Structure. ACM Transaction on Database Systems, 9(1), 1984.Google Scholar
  57. [57]
    P.V. Oösterom, and E. Claassen Orientation Insensitive Indexing Methods for Geometric Objects 4th International Symposium on Spatial Data Handling, Zurich, Switzerland p. 1016–1029, 1990.Google Scholar
  58. [58]
    A. V. Oppenheim and R.W. Schafer Digital Signal Processing Prentice Hall, Englewood Cliffs, N.J., 1975.MATHGoogle Scholar
  59. [59]
    J.A. Orestein Spatial Query Processing in an Object Oriented Database System. Proceedings of ACM SIGMOD Conference on the Management of Data, p. 326–336, Washington, U.S.A., May 1986.Google Scholar
  60. [60]
    M. Otterman Approximate Matching with High Dimensionality R-trees M.Sc. scholarly paper, Dept. of Computer Science, Univ, of Maryland, College Park, MD, 1992.Google Scholar
  61. [61]
    D. Papadias, and T. Sellis The Semantics of Relations in 2D Space Using Representative Points: Spatial Indexes In Frank, A.U., Campari, I. (eds.) Proceedings of the European Conference on Spatial Information Theory,COSITSpringer Verlag, 1993.Google Scholar
  62. [62]
    D. Papadias, and Y. Theodoridis Spatial Relations, Minimum Bounding Rectangles, and Spatial Data Structures Technical Report, KDBSLAB-TR-94–06, National Technical University of Athen, Greece, 1994.Google Scholar
  63. [63]
    D. Papadias, Y. Theodoridis, and T. Sellis The Retrieval of Direction Relations using R - trees In the Proceedings of the 5th International Conference on Databases and Expert Systems Applications, DEXA Springer Verlag, LNCS, 1994.Google Scholar
  64. [64]
    D. Papadias, Y. Theodoridis, T. Sellis, and M.J. Egenhofer Topological Relations in the World of Minimum Bounding Rectangles: A study with R-trees Proceedings of ACM SIGMOD International Conference on Management of Data, 1995.Google Scholar
  65. [65]
    C.H. Papadimitriou, D. Suciu, and V. Vianu Topological Queries in Spatial Databases ACM PODS, Montreal Quebec, Canada, 1996.Google Scholar
  66. D. Pequet, and Z. Ci-Xiang An Algorithm to Determine the Directional Relationship between Arbitrarily Shaped Polygons in the Plane Pattern Recognition, Vol. 20, No. 1, pp. 65–74Google Scholar
  67. [67]
    E. Persoon and K.S. Fu Shape discrimination using Fourier descriptors IEEE Transactions on Systems, Man and Cybernetics, 7, pp. 170–179, 1977.MathSciNetCrossRefGoogle Scholar
  68. [68]
    C.W. Richard Jr. and H. Hemami Identification of three--dimensional objects using Fourier descriptors of the boundary curve IEEE Transactions on Systems, Man and Cybernetics, SMC-4, pp. 371–378, July 1974.Google Scholar
  69. [69]
    Robinson, J.T. K — D — B tree: A Search Structure for Large Multidimensional Dynamic Indices. Proceedings of ACM SIGMOD Conference on the Management of Data, 1981.Google Scholar
  70. [70]
    M. Safar, and C. Shahabi 2D Topological and Direction Relations in the World of Minimum Bounding Circles In the Proceedings of IEEE 1999 International Database Engineering and uipplications Symposium (IDEAS), pp. 239–247, Montreal, Canada, August 2–4, 1999.Google Scholar
  71. [71]
    M. Safar, Shahabi C, and Sun X. Image Retrieval By Shape: A Comparative Study In the Proceedings of IEEE International Conference on Multimedia and Exposition (ICME), New York, U.S.A., July 30 - August 2, 2000.Google Scholar
  72. [72]
    M. Safar, Shahabi C, and Tan C.H. Resiliency and Robustness of Alternative Shape-Based Image Retrieval Techniques In the Proceedings of IEEE 2000 International Database Engineering and Applications Symposium (IDEAS), Japan, 2000.Google Scholar
  73. [73]
    M. Safar, Shahabi C. A Framework To Evaluate The Effectiveness Of Shape Representation Techniques Journal of Applied Systems Studies (JASS), special issue onDistributed Multimedia Systems and Applications”, 2001.Google Scholar
  74. [74]
    M. Safar, Shahabi C. Two Optimization Techniques to Improve the Performance of MBC-based Shape Retrieval In the Sixth International Workshop on Multimedia Information Systems (MIS), 2000.Google Scholar
  75. [75]
    A. Sajjanhar, G. Lu and J. Wright An experimental study of moment invariants and Fourier descriptors for shape based image retrieval Proceedings of the Second Australia Document Computing Symposium, Melbourne, Australia, pp. 46–54, April 5 1997.Google Scholar
  76. [76]
    A. Sajjanhar and G. Lu Indexing 2D non-occluded shape for similarity retrieval SPIE Conference on Applications of Digital Image Processing XX, Proceedings, Vol. 3164, San Diego, USA, pp.188–197, 30 July -1 August 1997.Google Scholar
  77. [77]
    A. Sajjanhar, and G. Lu A Grid Based Shape Indexing and Retrieval Method Special issue of Australian Computer Journal on Multimedia Storage and Archiving Systems, Vol. 29, No.4, pp. 131–140, November 1997.Google Scholar
  78. [78]
    A. Sajjanhar, and G. Lu A Comparison of Techniques for Shape Retrieval International Conference on Computational Intelligence and Multimedia Applications, Monash University, Gippsland Campus, pp.854–859, 9–11 Feb. 1998.Google Scholar
  79. [79]
    H. Samet The Design and Analysis of Spatial Data Structures. Addision-Wesley, 1989.Google Scholar
  80. [80]
    H. Samet Spatial Data Structures In Modern Database Systems: The Object Model, Interoperability and Beyond, W. Kim, e.d., Addison Wesley/ACM Press pp. 361–385, 1995.Google Scholar
  81. [81]
    B. Scassellatie, S. Alexopoulos, and M. Flickner Retrieving images by 2D shape: A comparison of computation methods with human perceptual judgments SPIE Conference on Storage and Retrieval for Image and Video Databases II, San Jose, CA, USA. SPIE Proceedings Vol. 2185, pp. 2–14, February 6–10, 1994.Google Scholar
  82. [82]
    B. Seeger, and H.P. Kriegel The Buddy Tree: An Efficient and Robust Access Method for Spatial Database Systems. Proceedings of 16th International Conference on Very Large Databases, Brisbone, Australia, p. 590–601,1990.Google Scholar
  83. [83]
    T. Sellis, N. Roussopoulos, and C. Faloutsos The R + Tree: A Dynamic Index for Multidimensional Objects. Proceedings of 13th International Conference on Very Large Databases, Brighton, U.K., p. 507–518. September, 1987.Google Scholar
  84. [84]
    C. Shahabi, M. Safar, and A. Hezhi Multiple Index Structures for Efficient Retrieval of 2D Objects In the Proceeding of IEEE 19th International Conference on Data Engineering (ICDE), Sydney, Australia, March 23–26, 1999.Google Scholar
  85. [85]
    C. Shahabi, M. Safar Efficient Retrieval and Spatial Querying of 2D Objects In the Proceedings of IEEE International Conference on Multimedia Computing and Systems (ICMCS), pp.611–617, June 7–11, Florence, Italy, 1999.Google Scholar
  86. [86]
    C. Shahabi, M. Safar, and X. Sun An Experimental Study of Alternative Shape-Based Image Retrieval Techniques Submitted to ACM Journal on Multimedia Systems, 2000.Google Scholar
  87. [87]
    K. Shim, R. Srikant, and R. Agrawal The ε-K-D-B tree:A Fast index Structure for High-dimensional Similarity Joins. Proceedings of the 13th International Conference on data engineering. Birmingham, U.K., April, 1997.Google Scholar
  88. [88]
    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, December 2000.Google Scholar
  89. [89]
    C.W. Sul, K.C. Lee, and K. Wohn Virtual Stage: A Location-Based Karoke System IEEE Multimedia, pp. 42–52, 1998.Google Scholar
  90. [90]
    Y. Tao, and W.I. Grosky Delaunay triangulation for image object indexing: a novel method for shape representation Proceedings of the Seventh SPIE Symposium on Storage and Retrieval for Image and Video Databases, San Jose, California, pp.631–642, January 1999.Google Scholar
  91. [91]
    Y. Tao, and W.I. Grosky Object-Based image retrieval using point feature maps Proceedings of the International Conference on Database Semantics (DS-8), Rotorua, New Zealand, pp. 59–73, January 1999.Google Scholar
  92. [92]
    Y. Theodoridis, D. Papadias, and E. Stefanakis Supporting Direction Relations in Spatial Database Systems Technical Report KDBSLAB-TR-95–02, National Technical university of Athens, Greece, 1995.Google Scholar
  93. [93]
    Y. Theodoridis, and T. Sellis On the Performance Analysis of Multi-dimensional R - tree - based Data Structures Technical Re-port KDBSLAB-TR-95–03, National Technical university of Athens, Greece, 1995.Google Scholar
  94. [94]
    A. Thomasian, V. Castello, and C.S. Li Clustering and Singular Value Decomposition for Approximate Indexing in High Dimensional Spaces. Proceedings of the ACM CIKM International Conference on Information and Knowledge Management, Bethesda, Maryland, USA, November 3–7, 1998.Google Scholar
  95. [95] T. Wallace and P.A. Wintz Fourier Descriptors for Extraction of Shape Information Final Report of Research for the Period Nov. 1, 1975 - Oct 31, 1976., Contract No. F 30602–75-C-0150.Google Scholar
  96. [96]
    T.P. Wallace, and P.A. Wintz An Efficient Three-Dimensional Aircraft Recognition Algorithm Using Normalized Fourier Descriptors Computer Graphics and Image Processing, 13, 99–126, 1980.CrossRefGoogle Scholar
  97. [97] Emo Welzl Smallest Enclosing Disks (Balls and Ellipsoids) “New Results and new Trends in Computer Science’) Lecture Notes in Com-puter 5Wence,555(359–370), 1991.Google Scholar
  98. [98]
    D. White, and R. Jain Similarity Indexing with the SS-Tree. Proceedings of the 12th International Conference on Data Engineering (ICDE), 1996.Google Scholar
  99. [99]
    K. Wu, A.D. Narasimhalu, B.M. Mehtre, C.P. Lam, and Y.J. Gao CORE: A content-based retrieval engine for multimedia information systems. Multimedia Systems, no. 3, p. 25–41, 1995.Google Scholar
  100. [100]
    C. Yang, and T. Lozano-Perez Image Database Retrieval with Multiple-Instance Learning Techniques In the Proceedings of the 16th International Conference on Data Engineering (ICDE), San Diego, CA, USA, February 29 - March 3, 2000.Google Scholar
  101. [101]
    Zhisheng You, and Anil K. Jain Performance Evaluation of Shape Matching via Chord Length Distribution Proceedings of Computer Vision, Graphics, and Image Processing, Vol. 28, pp. 129–142, 1984.Google Scholar
  102. [102]
    K. Zimmermann, and C. Freksa Qualitative Spatial Reasoning Using Orientation, Distance, and Path Knowledge IJCAI Workshop on Spatial and Temporal Reasoning, Chambery, August 1993.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Maytham H. Safar
    • 1
  • Cyrus Shahabi
    • 2
  1. 1.Kuwait UniversityKuwait
  2. 2.University of Southern CaliforniaUSA

Personalised recommendations