Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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An Improvement Video Search Method for VP-Tree by using a Trigonometric Inequality

  • Lee, Samuel Sangkon (Dept. of Computer Science and Engineering, Jeonju University) ;
  • Shishibori, Masami (Institute of Technology and Science, The University of Tokushima) ;
  • Han, Chia Y. (Dept. of Computer Science, College of Engineering and Applied Science, University of Cincinnati)
  • Received : 2012.05.07
  • Accepted : 2013.02.22
  • Published : 2013.06.29
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Abstract

This paper presents an approach for improving the use of VP-tree in video indexing and searching. A vantage-point tree or VP-tree is one of the metric space-based indexing methods used in multimedia database searches and data retrieval. Instead of relying on the Euclidean distance as a measure of search space, the proposed approach focuses on the trigonometric inequality for compressing the search range, which thus, improves the search performance. A test result of using 10,000 video files shows that this method reduced the search time by 5-12%, as compared to the existing method that uses the AESA algorithm.

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References

  1. K. Kita, K. Tsuda, and M. Shishibori, Information Retrieval Algorithm, Kyoritsu Shuppan, 2002, pp.5-25.
  2. M. Yoshikawa, and S. Uemura, "Indexing Techniques for Multimedia Data," Journal of Information Processing Society of Japan, Vol.42, No.10, 2001, pp.953-957.
  3. N. Katayama, and S. Satoh, "Indexing Techniques for Similarity Retrieval," Journal of Information Processing Society of Japan, Vol.42, No.10, 2001. pp.958-963.
  4. A. Guttman, "A Dynamic Index Structure for Spatial Searching," Proceedings of the ACM SIGMOD, Boston, MA, 1984, pp.47-57.
  5. N. Beckmann, H. -P. Kriegel, R. Schneider, and B. Seeger, "The R*-tree: An Efficient and Robust Access Method for Points and Rectangles," Proceedings of the ACM SIGMOD, Atlantic City, NJ, 1990, pp.322-331.
  6. D. A. White, and R. Jain, "Similarity Indexing with SS-tree," Proceedings of the 12th International Conference on Data Engineering, 1996, pp.516-523.
  7. N. Katayama, and S. Satoh, "SR-Tree : An Index Structure for Nearest Neighbor Searching of High-Dimensional Point Data," IEICE Trans. on Information and Systems, Vol.J80-D-I, No.8, 1997, pp.703-717.
  8. S. Berchtold, D. A. Keim, and H. -P. Kriegel, "The X-tree An Index Structure for High Dimensional Data," Proceedings of the 22nd VLDB, 1996, pp.28-39.
  9. R. Weber, F. J. Schek, and S. Blott, "A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces," Proceedings of the 24th VLDB, 1998, pp.194-205.
  10. M. Ioka, A Method of Defining the Similarity of Images on the Basis of Color Information, Technical Report RT0030, IBM Tokyo Research Lab., 1989.
  11. Y. Rubner, C. Tomasi, and L. J. Guibas, "The Earch Mover's Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval," Proceedings of the ARPA Image Understanding Workshop, 1999, pp.661-668.
  12. P. Ciaccia, M. Patella, and P. Zezula, "M-tree: An Efficient Access Method for Similarity Search in Metric Spaces," Proceedings of the ACM SIGMOD, San Jose, CA, 1995, pp.71-79.
  13. P. N. Yianilos, "Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces," Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, 1993, pp.311-321.
  14. A. W. -C. Fu, P. M. S. Chan, Y. L. Cheung, and Y. S. Moon, "Dynamic VP-Tree Indexing for N-Nearest Neighbor Search Given Pair-Wise Distances," The VLDB Journal, Vol.9, No.2, 2000, pp.154-173. https://doi.org/10.1007/PL00010672
  15. T. Bozkaya, and M. Ozsoyoglu, "Distance-based Indexing for High Dimensional Metric Spaces," Proceedings of the 1997 ACM SIGMOD, Tucson, AZ, 1997, pp.357-368.
  16. M. Ishikawa, J. Notoya, H. Chen, and N. Ohbo, "A Metric Index MItree," Trans. of Information Pro-cessing Society of Japan, Vol.40, No.SIG 6 (TOD 3), 1999, pp.104-114.
  17. V. Ruiz, "An Algorithm for Finding Nearest Neighbors in (approximately) Constant Average Time," Pattern Recognition Letters, Vol.4, iss.3, 1986, pp.145-157. https://doi.org/10.1016/0167-8655(86)90013-9
  18. M. Iwasaki, "Implementation and Evaluation of Metric Space Indices for Similarity Search," Trans. of Information Processing Society of Japan, Vol.40, No.SIG 3(TOD 1), 1999, pp.24-33.
  19. H. Akama, F. Konishi, T. Yoshida, M. Yamamuro, and K. Kushima, "External Key Search and Dy-namic Data Insertion in Inverted File Indexing Method Applied for Nearest Neighbor Search," Trans. of Information Processing Society of Japan, Vol.40, No.SIG 8(TOD 4), 1999, pp.51-62.
  20. Corel Image, http://www.corel.com, 2011.
  21. M. Shishibori, S. S. Lee, and K. Kita, "A Method to Improve Metric Index VP-tree for Multimedia Databases," The 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Woolsan, Ko-rea, 2011, pp.21-26.
  22. P. Zezula, G. Amato, V. Dohnal, M. Batko, Similarity Search: The Metric Space Approach, 1st ed., Springer, New York, 2005, pp.9-16.
  23. M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni, "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions," Proceedings of the Twentieth Annual Symposium on Computational Geometry (SCG '04), 2004, pp.253-262.
  24. T. Darrell and P. Indyk and G. Shakhnarovich (eds.), "Nearest Neighbor Methods in Learning and Vision: Theory and Practice," MIT Press, 2006.
  25. A. Andoni and P. Indyk, "Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions," Communications of the ACM, Vol.51, No.1, 2008, pp.117-122.
  26. A. Andoni and P. Indyk "Near-Optimal Hashing Algorithms for Near Neighbor Problem in High Dimensions," In Proceedings of the Symposium on Foundations of Computer Science (FOCS '06), 2006, pp.459-468.

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