Multidimensional indexing structures for content-based image retrieval: A survey

International Journal of Innovative Computing, Information and Control

Volumn 4, Issue 4, 2008, Pages 867-881

  Sudhamani, M V ^a   Venugopal, C R ^b  

^a SIDDAGANGA INSTITUTE OF TECHNOLOGY   (India)

^b SRI JAYACHAMARAJENDRA COLLEGE OF ENGINEERING   (India)

Author keywords

Content based; Features; Image retrieval; Indexing; Queries

Indexed keywords

EID: 48349135096     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (100)

1. O. Gunther and Buchmann, Research issues in spatial databases, SIGMOD, vol.19, no.4, pp.61-68, 1990.
2. R. Bayer and McCreight E. M, Organization and maintenance of large ordered indices, Acta Informatica, vol.1, no.3, pp.173-189, 1972.
3. R. Fagin, J. Nievergelt, N. Pippenger and R. Strong, Extendible hashing: A fast access method for dynamic files, ACM Trans. Database Systems, vol.4, no.3, pp.315-344, 1979.
4. H. P. Kriegel, Performance comparison of index structures for multikey retrieval, Proc. of the ACM SIGMOD, Int. Conf. on Management of Data, pp.186-196, 1984.
5. B. C. Ooi, Query processing in geographic information systems, Springer-Verlag, Berlin/Heidelberg/New York, LNCS 471, 1990.
6. C. Kolovson, Indexing Techniques for Multi-dimensional Spatial Data and Historical Data in Database Management Systems, Ph. D. Thesis, University of California, Berkeley, 1990.
7. P. Oosterom, Data Structures for Geographic Information Systems, Ph.D. Thesis, University of Leiden, The Netherlands, 1990.
8. M. Schiwietz, Speicherung and Anfragebearbeitung Komplexer Geo-objekte, Ph.D. Thesis, Ludwig-Maximilians University, Munchen, 1993.
9. P. Widmayer, Datenstrukturen fur geodatenbanken, in Entwicklungstendenzen bei Datenbank-systemen, G. Vossen and K.-U. Witt (eds.), Munich: Oldenbourg-Verlag, Chapter 9, pp.317-361, 1991.
10. H. Samet, Applications of Spatial Data Structures, Addison-Wesley, Reading, Mass., 1990.
11. H. Samet, The Design and Ana. of Spatial Data Struc., Addison-Wesley, Reading, Mass., 1990.
12. D. B. Lomet, Grow and post index trees: Role, techniques and future potential, in Advances in Spatial Databases, O. Gunther and H. Schek (eds.), Berlin/Heidelberg/NY, Springer-Verlag, 1991.
13. J. T. Robinson, The K-D-B-tree: A search structure for large multidimensional dynamic indexes, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pp.10-18, 1981.
14. D. B. Lomet and B. Salzberg, The hB-tree: A robust multiattribute search structure, Proc. of the 5th IEEE Int. Conf. on Data Eng., pp.296-304, 1989.
15. J. Nievergelt, 7 Sigma 2 criteria for assessing and comparing spatial data structures, in Design and Implementation of Large Spatial Databases, A. Buchmann, O. Gunther, T. R. Smith and Y. F. Wang (eds.), Berlin/Heidelberg/New York, Springer-Verlag, LNCS 409, 1990.
16. W. Niblac, K. R. Barber, W. Equitz, M. Flic Kner, E. Glas Man, D. Petkovic, P. Y. Anker, C. F. Aloutsos and G. Taubin, The QBIC project: Querying images by content usingcolor texture, and shape, Proc. SPIE, Storage and Retrieval for Image and Video Databases, vol.1908, pp.173-187, 1993.
17. V. Castelli, Multidimensional indexing struc for CBR, IBM Research Report, 2001.
18. B. V. Bonnlander and A. S. Weigend, Selecting input variables using mutual information and non-parametric density estimation, Proc. Int. Symp. Artificial Neural Networks, pp.42-50, Tainan, Taiwan, 1994.
19. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, 2nd edition, 1990.
20. R. A. Horn and C. R. Johnson, Matrix Analysis, Cambridge University Press, 1992.
21. I. T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 1986.
22. R. Ng and A. Sedighian, Evaluating multi-dimensional indexing structures for images transformed by principal component analysis, Proc. SPIE, Storage and Retrieval for Still Image Video Databases, vol.2670, pp.50-61, San Jose, CA, USA, 1996.
23. A. R. Webb, Multidimensional scaling by iterative majorization using radial basis functions, Pattern Recognition, vol.28, no.5, pp.753-759, 1995.
24. M. Beatty and B. S. Manjunath, Dimensionality reduction using multi-dimensional scaling for content-based retrieval, Proc. of the IEEE Int. Conf. Image Processing, Santa Barbara, CA, vol.2, pp.835-838, 1997.
25. A. Califano and R. Mohan, Multidimensional indexing for recognizing visual shapes, Proc. IEEE Computer Vision and Pattern Recognition, pp.28-34, 1991.
26. H. Wolfson, Model-based object recognition by geometric hashing, Proc. of the 1st European Conf. Com. Vision, pp.526-536, 1990.
27. G. Knot, Hashing functions, The Computer Journal, vol.18, no.3, pp.265-278, 1975.
28. D. Comer, The ubiquitous B-tree, ACM Comp. Survey, vol.11, no.2, pp.121-138, 1979.
29. W. Litwin, Linear hashing: A new tool for file and table addressing, Proc. of the 6th Int. Conf. on Very Large Data Bases, pp.212-223, 1980.
30. P. A. Larson, Linear hashing with partial expansions, Proc. of the 6th Int. Conf. on Very Large Data Bases, pp.224-232, 1980.
31. J. Orenstein, A dynamic file for random and sequential accessing, Proc. of the 9th Int. Conf. on Very Large Data Bases, pp.132-141, 1983.
32. A. K. Garg and C. C. Gotlieb, Order-preserving key transformation, ACM Trans. Database Systems, vol.11, no.2, pp.213-234, 1986.
33. J. L. Bentley, Multidimensional binary search in database applications, IEEE Trans. Software Engineering, vol.4, no.5, pp.333-340, 1979.
34. J. T. Robinson, The k-d-b-tree: A search structure for large multidimensional dynamic indexes, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pp.10-18, 1981.
35. D. Lomet and B. Salzberg, The hB-tree: A multiattribute indexing method with good guaranteed performance, ACM Trans. Database Systems, vol.15, no.4, pp.625-658, 1990.
36. D. Lomet, DL* -Trees, Research Report RC-10860, IBM T. J. Watson Res. Ctr., Yorktown Heights, New York, 1984.
37. T.M. Chan, Approximate nearest neighbor queries revisited, Proc. of the 13th ACM Symp. Computational Geometry, pp.352-358, 1997.
38. S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman and A. Y. Wu, An optimal algorithm for approximate nearest neighbor searching in fixed dimensions, J. ACM, vol.45, no.6, pp.891-923, 1998.
39. M. Tamminen, Comment on quad- and octrees, Comm. ACM 30, vol.3, pp.204-212, 1984.
40. W. C. Thibault and B. F. Naylor, Set operations on polyhedra using binary space partitioning trees, Computer Graphics, vol.21, no.4, pp.153-162, 1987.
41. B. S. Kim and S. B. Park, A fast k nearest neighbor finding algorithm based on the ordered partition, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.8, no.6, pp.761-766, 1986.
42. B. C. Ooi, R. Sacks Davis and K. J. McDonell, Spatial k-d-tree: An indexing mechanism for spatial databases, Proc. of the IEEE COMPSAC Conf, 1987.
43. Y. Ohsawa, and M. Sakauchi, BD-tree: A new N-dimensional data structure with efficient dynamic characteristics, Proc. of the 9th World Computer Congress, IFIP, pp.539-544, 1983.
44. Y. Ohsawa and Sakauchi, A new tree type data structure with homogeneous node suitable for a very large spatial database, Proc. of the 6th IEEE Int. Conf. on Data Eng, pp.296-303, 1990.
45. A. Henrich, H. W. Six and P. Widmayer, The LSD tree: Spatial access to multidimensional point and non-point objects, Proc. of the 15th Int. Conf. on Very Large Data Bases, pp.45-53, 1989.
46. H. Samet, Quadtree and related hierarchical data structures, ACM Computing Surveys, vol.16, no.2, pp.187-260, 1984.
47. R. A. Finkel and L. J. Bentley, Quad trees, a data structure for retrieval on composite keys, Acta Informatica, vol.4, no.1, pp.1-9, 1974.
48. I. Gargantini, An effective way to represent quadtrees, Communications of the ACM, vol.25, no.12, pp.905-910, 1982.
49. C. P. Kolovson and M. Stonebraker, Segment indexes: Dynamic indexing techniques for multi-dimensional interval data, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pp.138-147, 1991.
50. T. Tzouramanis, M. Vassilakopoulos and Y. Manolopoulos, Overlapping linear quadtrees: a spatio-temporal access method, Proc. 6th Int. Symp. Advances in geographic information systems, pp.1-7, 1998.
51. W. G. Aref and H. Samet, Efficient processing of window queries in the pyramid data structure, Proc. Ninth ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, pp.265-272, 1990.
52. H. Samet and R. E. Webber, Storing a collection of polygons using quadtrees, ACM Trans. Graphics, vol.4, no.3, pp.182-222, 1985.
53. H. Samet, Hierarchical representation of collections of small rectangles, ACM Comp. Surv, vol.20, no.4, pp.271-309, 1988.
54. A. Guttman, R-Trees: A dynamic index structure for spatial searching, ACM SIGMOD Record, vol.14, no.2, pp.47-57, June 1984.
55. D. Greene, An implementation and performance analysis of spatial access data methods, Proc. of the 5th Int. Conf. on Data Engineering, Los Angeles, California, pp.606-615, 1989.
56. T. Sellis, N. Roussopoulos and C. Faloutsos, The R+-tree: A dynamic index for multidimensional objects, Proc. of the 13th Int. Conf. on Very Large Data Bases VLDB, Brighton, England, pp.507-518, 1987.
57. N. Beckmann, H. P. Kriegel, R. Shneider and B. Seeker, The R2, - tree: An efficient and robust access method for points and rectangles, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, USA, pp.322-331, 1990.
58. N. Rousopoulos and D. Leifker, Direct spatial search on pictorial databases using packed R-trees, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pp.17-31, 1985.
59. I. Kamel and C. Faloutsos, On packing r-trees, Proc. of the 3rd Int. Conf. Information and Knowledge Management CIKM, Washington DC, pp.490-499, 1993.
60. J. Orenstein, Spatial query processing in an object-oriented database system, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pp.326-336, 1986.
61. C. Faloutsos,Gray codes for partial match and range queries, IEEE Trans. Software Engineering, vol.14, no.10, pp.1381-1393, 1988.
62. C. Faloutsos and S. Roseman,Fractals for secondary key retrieval, Proc. of the 8th ACM ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, Philadelphia, PA, USA, pp.247-252, 1989.
63. I. Kamel and C. Faloutsos, R-tree: An improved R-tree using fractals, Proc. of the 20th Int. Conf. on Very Large Data Bases VLDB, Santiago, Chile, pp.500-509, 1994.
64. S. Berchtold, D. A. Keim, and H.-P. Kriegel, The X-tree: An index structure for high-dimensional data, Proc. of the 19th Int. Conf. on Very Large Data Bases VLDB, Bombay, India, pp.28-39, 1996.
65. D. A. White and R. Jain, Similaxity indexing: Algorithms and performance, Proc. SPIE, Storage and Retrieval for Still Image Video Databases, San Diego, USA, vol.2670, pp.62-73, 1996.
66. C. Aggarwal, J. L. Wolf, P. S. Yu and M. Epelman, The S-Tree: An efficient index for multidimensional objects, Proc. Symp. Large Spatial Databases, Lecture Notes in Computer Science, Berlin, Germany, Springer, vol.1262, pp.350-373, 1997.
67. O.G unther and E. Wong, A dual-space representation for geometric data, Proc. of the 13th Int. Conf. on Very Large Data Bases VLDB, Brighton, U. K., pp.501-506, 1987.
68. H. V. Jagadish, Spatial search with polyhedra, Proc. of the 6th Int. Conf. on Data Engineering, pp.311-319, 1990.
69. D. A. White and R. Jain, Similarity indexing with the SS-Tree, Proc. of the 12th Int. Conf. on Data Engineering, New Orleans, USA, pp.516-523, 1996.
70. N. Katayama and S. Satoh, SR-tree: An index structure for high-dimensional nearest neighbor query, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Tucson,AZ, pp.369-380, 1997.
71. K. I. Lin, H. V. Jagadis and C. F. Aloutsos, The TV-tree: and index structure for high-dimensional data, VLDB Journal, vol.3, no.4, pp.517-542, 1994.
72. J. Nievergelt, H. Hinterberger and K. C. Sevcik, The grid file: An adaptable, symmetric multikey file structure, in Proc. 3rd ECI Conf., A. Duijvestijn and P. Lockemann (eds.), LNCS 123, Berlin, Heidelberg, New York, Springer-Verlag, pp.236-251, 1981.
73. K. Hinrichs, Implementation of the grid file: Design concepts and experience, BIT, vol.25, pp.569-592, 1985.
74. K. Y. Whang and R. Krishnamurthy, Multilevel Grid files, Technical Report, IBM Research Lab., Yorktown Heights, New York, 1985.
75. H. Blanken, A. Ijbema, P. Meek and B. van den Akker, The generalized grid file: Description and performance aspects, Proc. of the 6th IEEE Int. Conf. on Data Eng, pp.380-388, 1990.
76. M. Tamminen, Performance analysis of cell based geometric file organizations, Int. J. Comp. Vision, Graphics and Image Processing, vol.24, pp.160-181, 1983.
77. K. Hinrichs, Implementation of the grid file: Design concepts and experience, BIT, vol.25, pp.569-592, 1985.
78. A. Hutflesz, H. W. Six and P. Widmayer, Twin grid files: Space optimizing access schemes, Proc. of the ACM SIGMOD Int. Conf. on Management of Data, 1988b.
79. B. Seeger and H. P. Kriegel, Techniques for design and implementation of spatial access methods, Proc. of the 14th Int. Conf. on Very Large Data Bases, pp.360-371, 1988.
80. J. Orenstein and T. H. Merrett, A class of data structures for associative searching, Proc. 3rd ACM SIGA CT-SIGMOD Symp. on Principles of Database Systems, pp.181-190, 1984.
81. B. Seeger and H. P. Kriegel, The Buddy - tree: An efficient and robust access method for spatial data base systems, Proc of the 16th Int. Conf. on Very Large Data Bases, pp.590-601, 1990.
82. M. Freeston, The BANG file: A new kind of grid file, Proc. of the ACM SIG. Int. Conf. on Mant. of Data, pp.260-269, 1987.
83. H. P. Kriegel and B. Seeger, Multidimensional quantile hashing is very efficient for non-uniform distributions, Information Sciences, vol.48, pp.99-117, 1989.
84. M. Reeston, A general solution of the n-dimensional B-tree problem, Proc. of the ACM SIGMOD, Int. Conf. on Management of Data, pp.80-91, 1995.
85. H. P. Kriegel and B. Seeger, Multidimensional order preserving linear hashing with partial expansions, Proc. of the Int. Conf. on Database Theory, New York, Springer-Verlag, vol.243, 1986.
86. A Comparative Study of Spatial Indexing Techniques for Multidimensional Scientific Datasets, Technical Report CS-TR-4556 and UMIACS-TR-2004-03, University of Maryland, January, 2004.
87. A. Hutflesz, H. W. Six and P. Widmayer, R-file: An efficient access structure for proximity queries, Proc. of the 6th IEEE Int. Conf. on Data Eng, pp.372-379, 1990.
88. H. Sagan, Space-Filling Curves, Springer-Verlag, Berlin, Heidelberg, New York, 1994.
89. D. J. Abel and D. M. Mark, A comparative analysis of some twodimensional orderings, Int. J. Geographical Information Systems, vol.4, no.1, pp.21-31.
90. C. V. Remco and T. Mirela Tanase, Content-based image retrieval systems: A survey, Technical Report UU-CS-2000-34, Department of Computing Science, Utrecht University, October, 2002.
91. M. Paisarn and G. Ling, Automatic machine Interactions for content-based image retrieval using a self-organizing tree map architecture, IEEE Trans. on Neu. Nets, vol.13, no.4, 2002.
92. D. Sagarmay, Experiments on emergence index in content-based image retrieval, Proc. of the 3rd Int. Conference on Information Technology and Applications, IEEE, 2005.
93. J. Li, Z. W. James, Real-time computerized annotation of picture, ACM 1-59593-447-2/06/0010, October, 2006.
94. S. Kiranyaz and M. Gabbouj, Hierarchical cellular tree: An efficient indexing scheme for content-based retrieval on multimedia databases, IEEE Tra. Mul. Media, vol.9, no.1, 2007.
95. Y. Chen, J. Z. Wang and R. Krovetz, CLUE: Cluster-based retrieval of images by unsupervised learning, IEEE Transactions on Image Processing, vol.14, no.8, 2005.
96. Khanh Vu. et.al., Image retrieval based on regions of interest, IEEE Transactions on Knowledge Engineering, vol.15, no.4, 2003.
97. Apostolnatsev, R. Rastogi, and K. Shim, WARLUS: A similarity retrieval algorithm for image databases, IEEE Trans. on Knowledge and Data Engg., vol.16, no.3, 2004.
98. Indexing cached multidimensional objects in large main memory systems, Technical Report CS-TR-and UMIACS-TR, University of Maryland, January, 2006.
99. Y. Qiao, Z. Lu, J. Pan and S. Sun, Spline wavelets based texture features for image retrieval, International Journal of Innovative Computing, Information and Control, vol.2, no.3, pp.653-658, 2006.
100. W. M. Zheng, Z. M. Lu and H. Burkhardt, Color image retrieval schemes using index histograms based on various spatial-domain vector quantizers, International Journal of Innovative Computing, Information and Control, vol.2, no.6, pp.1317-1326, 2006.