Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Advanced indexing schema for imaging applications: three case studies

Advanced indexing schema for imaging applications: three case studies

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

Imaging techniques and applications often require heavy computations for finding the k-nearest-neighbour of a given pattern. Texture synthesis, image colourisation and super-resolution are all affected by this issue. Advanced clustering-based indexing schemas over metric spaces speed-up efficiently both k-nearest-neighbour and range searches. By using them, we are able to save CPU time without losing quality which would be lost using approximate approaches. Moreover, with the proposed technique we are able to convert a batch process task into a real-time task and, more importantly, it might be run on a typical user-end PC desktop rather than powerful mainframes. It has been shown how the application of recently reported well-known indexing schemas improves the speed performance of the above problems.

Inspec keywords: image resolution; image colour analysis; visual databases; pattern clustering; image texture; database indexing

Other keywords: image texture synthesis; image colourisation; image quality; k-nearest-neighbour pattern clustering; clustering-based indexing schema; image super-resolution; imaging application

Subjects: Spatial and pictorial databases; Optical, image and video signal processing; Computer vision and image processing techniques

References

    1. 1)
      • A. Gersho , R.M. Gray . (1991) Vector quantization and signal compression.
    2. 2)
      • Ciaccia, P., Patella, M., Zezula, P.: `M-tree: an efficient access method for similarity search in metric spaces', Proc. 23th Int. Conf. on Very Large Databases, 1997, p. 426–435.
    3. 3)
      • C. Aggarwal , J.L. Wolf , P.S. Yu , M. Epelman . Using unbalanced trees for indexing multidimensional objects. Knowl. Inf. Syst. , 3 , 309 - 336
    4. 4)
    5. 5)
      • Chazelle, B.: `Computational geometry: a retrospective', Proc. 26th Annual ACM Symp. Theory of Computing, 1994, p. 75–94.
    6. 6)
      • Ciaccia, P., Patella, M.: `Pac nearest neighbor queries: approximate and controlled search in high-dimensional and metric spaces', ICDE, 2000, p. 244–255.
    7. 7)
      • D.S. Ebert , F.K. Musgrave , D. Peachey , K. Perlin , S. Worley . (1998) Texturing and modeling: a procedural approach.
    8. 8)
      • T. Mitchell . (1997) Machine learning.
    9. 9)
      • Di Blasi, G., and Reforgiato Recupero, D.: ‘Fast colorization of gray images'. Eurographics Italian Chapter, 2004.
    10. 10)
      • W.F. Schreiber . (1986) Fundamentals of eletronic imaging systems.
    11. 11)
      • Heeger, D.J., Bergen, J.R.: `Pyramid-based texture analysis/synthesis', Computer Graphics, ACM SIGGRAPH, 1995, p. 229–238.
    12. 12)
      • K. Perlin . An image synthesizer. Proc. SIGGRAPH’ 85 , 287 - 296
    13. 13)
      • Wei, L.Y., Levoy, M.: `Fast texture synthesis using tree-structured vector quantization', Proc. ACM-SIGGRAPH, 2000, p. 479–488.
    14. 14)
      • Juan, A., Vidal, E.: `An optimized version of the approximating and eliminating search algorithm (AESA) for nearest neighbour classification', Technical report ITI-ITE-3/98, 1998.
    15. 15)
      • F. Moreno-Seco , J. Oncina , L. Mico' . Improving the linear approximating and eliminating search algorithm (LAESA) error rates. Pattern Recognit. Appl. , 37 - 43
    16. 16)
      • Chávez, E., Navarro, G.: `An effective clustering algorithm to index high dimensional metric spaces', Proc. SPIRE, 2000, p. 75–86.
    17. 17)
    18. 18)
      • Indyk, P., Motwani, R.: `Approximate nearest neighbors: towards removing the curse of dimensionality', Proc. 30th Annual ACM Symp. Theory of Computing, 1998, p. 604–613.
    19. 19)
      • Gionis, A., Indyk, P., Motwani, R.: `Similarity search in high dimensions via hashing', Proc 25th VLDB Conf., 1999, p. 518–529.
    20. 20)
      • Fekri, F., Mersereau, R.M., Schafer, R.W.: `A generalized interpolative vq method for jointly optimal quantization and interpolation of images', Proc. Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 1998, 5, p. 2657–2660.
    21. 21)
      • Traina, C., Traina, A., Seeger, D., Faloutsos, C.: `Slim-trees: high performace metric trees minimizing overlap between nodes', Proc. 7th Int. Conf. Extending Database Technology, 2000, p. 51–56, (Lect. Notes Comput. Sci., 1777).
    22. 22)
      • Simoncelli, E., Portilla, J.: `Texture characterization via joint statistics of wavelet coefficient magnitudes', Fifth Int. Conf. Image Processing, 1998, 1, p. 62–66.
    23. 23)
    24. 24)
    25. 25)
      • Silberg J.: http://www.cinesite.com/core/press/articles/1998/10_00_98- team.html1998.
    26. 26)
      • Wei, L.Y., Levoy, M.: `Order-independent texture synthesis', Technical report, 2002.
    27. 27)
      • Moreno-Seco, F., Oncina, J., Mico', L.: `Improving the LAESA algorithm error rates', Proc. VIII Symp. NdRdFyAdI, 1999, 1, p. 413–419.
    28. 28)
      • University of Washington Data Compression Laboratory: ‘TSVQ’. http://dcl.ee.washington.edu/.
    29. 29)
      • Chávez, E., Navarro, G.: `A probabilistic spell for the curse of dimensionality', Proc. 3rd Workshop on Algorithm Engineering and Experimentation (ALENENEX'01), 2001, 2153, p. 147–160, Lect. Notes Comput. Sci.
    30. 30)
      • Figueroa, K., Chavez, E., Navarro, G., Paredes, R.: `On the least cost for proximity searching in metric spaces', WEA 2006, 2006, p. 279–290.
    31. 31)
      • E. Reinhard , M. Ashikhmin , B. Gooch , P. Shirley . Color transfer between images. IEEE Comput. Graph. Appl. , 34 - 40
    32. 32)
      • G.R. Hjaltason , H. Samet . Distance browsing in spatial database. ACM Trans. Inf. Syst. , 2 , 265 - 318
    33. 33)
      • Rogowitz, B.E., Kalvin, A.D.: `The “Which Blair Project”: a quick visual method for evaluating perceptual color maps', Proc. IEEE Visualization 2001, 2001.
    34. 34)
    35. 35)
      • K. Popat , R. Picard . Novel cluster-based probability model for texture synthesis, classification, and compression. Proc. SPIE , 756 - 768
    36. 36)
      • K. Beyer , J. Goldstein , R. Ramakrishnan , U. Shaft . (1999) When is nearest neighbor meaningful?’. Proc. 7th Int. Conf. Database Theory.
    37. 37)
      • Peachey, D.: `Solid texturing of complex surfaces', Proc. SIGGRAPH’ 85, 1985, p. 279–286.
    38. 38)
      • Sumanasekara, S., Ramakrishna, M.V.: `Chilma: an efficient high dimensional indexing structure for image database', Proc. First IEEE Pacific-Rim Conf. Multimedia, 2000, p. 76–79.
    39. 39)
      • Battiato, S., Pulvirenti, A., Reforgiato, D.: `Antipole clustering for fast texture synthesis', Proc. Winter School of Computer Graphics (WSCG), 2003.
    40. 40)
      • Bustos, B., Navarro, G.: `Probabilistic proximity searching algorithms based on compact partitions', Proc. SPIRE, 2002, p. 284–297.
    41. 41)
      • Di Blasi, G., Gallo, G., Petralia, M.: `Smart ideas for photomosaic rendering', Proc. Eurographics Italian Chapter 2006, 2006, p. 267–271.
    42. 42)
      • E. Chávez , G. Navarro , R. BaezaYates , J.L. Marroquin . Searching in metric spaces. ACM Comput. Surv. , 3 , 273 - 321
    43. 43)
      • Baeza-Yates, R., Cunto, W., Manber, U., Wu, S.: `Proximity matching using fixed-queries trees', Proc. Combinatorial Pattern Matching, 5th Annual Symp., 1994, p. 198–212, (Lect. Notes Comput. Sci., 807).
    44. 44)
      • D. Cantone , A. Ferro , A. Pulvirenti , D. Reforgiato , D. Shasha . Antipole tree indexing to support range search and k-nearest-neighbor search in metric spaces. IEEE Trans. Knowl. Data Eng. , 4 , 535 - 550
    45. 45)
      • W. Freeman , E. Pasztor , O. Carmichael . Learning low-level vision. Int. J. Comput. Vis. , 1 , 25 - 47
    46. 46)
      • Berchtold, S., Keim, D.A., Kriegel, H.P.: `The x-tree: an index structure for high-dimensional data', Proc. 22th Int. Conf. Very Large Database, 1996, p. 28–39.
    47. 47)
    48. 48)
    49. 49)
      • Welsh, T., Ashikmin, M., Mueller, K.: `Transferring color to greyscale images', Proc. ACM SIGGRAPH 2002, 2002.
    50. 50)
      • Ashikhmin, M.: `Synthesizing natural textures', ACM Symp. Interactive 3D Graphics, 2001, p. 217–226.
    51. 51)
    52. 52)
      • Di Blasi, G., Gallo, G., Petralia, M.: `Puzzle image mosaic', Proc. IASTED/VIIP2005, 2005.
    53. 53)
      • Hertzmann, A., Jacobs, C.E., Olivier, N., Curless, B., Salesin, D.H.: `Image analogies', ACM SIGGRAPH, 2001.
    54. 54)
      • Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: `Image analogies', Proc. ACM SIGGRAPH 2001, 2001.
    55. 55)
      • Freeman, W.T., Jones, T.R., Pasztor, E.C.: `Example-based superresolution', 2001, MERL TR-2001-340.
    56. 56)
      • W.T. Freeman , E.C. Pasztor . Learning to estimate scenes from images. Adv. Neural Information Process. Syst. , 775 - 781
    57. 57)
      • , : `Super-resolution', Technical report, 2002, 24 Régis Destobbeleire:.
    58. 58)
    59. 59)
      • De Bonet, J.S.: `Multiresolution sampling procedure for analysis and synthesis of texture images', Proc. ACM SIGGRAPH, 1997, p. 361–368.
    60. 60)
      • Efros, A.A., Freeman, W.T.: `Image quilting for texture synthesis and transfer', Proc. SIGGRAPH 2001, 2001.
    61. 61)
      • A. Efros , T. Leung . Texture synthesis by a non-parametric sampling. Proc. IEEE Int. Conf. Comput. Vis. , 1033 - 1038
    62. 62)
      • R.C. Gonzales , R.E. Woods . (1993) Digital image processing.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr_20060315
Loading

Related content

content/journals/10.1049/iet-ipr_20060315
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address