Skip to main content
SpringerLink
Log in

A Progressive Approach for Similarity Search on Matrix

  • Conference paper
  • First Online:
Advances in Spatial and Temporal Databases (SSTD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9239))

Included in the following conference series:

Abstract

We study a similarity search problem on a raw image by its pixel values. We call this problem as matrix similarity search; it has several applications, e.g., object detection, motion estimation, and super-resolution. Given a data image D and a query q, the best match refers to a sub-window of D that is the most similar to q. The state-of-the-art solution applies a sequence of lower bound functions to filter sub-windows and reduce the response time. Unfortunately, it suffers from two drawbacks: (i) its lower bound functions cannot support arbitrary query size, and (ii) it may invoke a large number of lower bound functions, which may incur high cost in the worst-case. In this paper, we propose an efficient solution that overcomes the above drawbacks. First, we present a generic approach to build lower bound functions that are applicable to arbitrary query size and enable trade-offs between bound tightness and computation time. We provide performance guarantee even in the worst-case. Second, to further reduce the number of calls to lower bound functions, we develop a lower bound function for a group of sub-windows. Experimental results on image data demonstrate the efficiency of our proposed methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This is similar to the division of nodes in a quadtree.

  2. 2.

    In general, the space \([1..L_q,1..W_q]\) may have less than \(O(4^{\ell })\) disjoint rectangles.

References

  1. Weather datasets. http://weather.is.kochi-u.ac.jp/sat/GAME/

  2. Ben-Artzi, G., Hel-Or, H., Hel-Or, Y.: The gray-code filter kernels. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 382–393 (2007)

    Article  Google Scholar 

  3. Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: index structures for improving the performance of multimedia databases. ACM Comput. Surv. 33(3), 322–373 (2001)

    Article  Google Scholar 

  4. Brad, R., Letia, I.A.: Extracting cloud motion from satellite image sequences. In: ICARCV, pp. 1303–1307 (2002)

    Google Scholar 

  5. Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access method for similarity search in metric spaces. In: VLDB, pp. 426–435 (1997)

    Google Scholar 

  6. Dufour, R.M., Miller, E.L., Galatsanos, N.P.: Template matching based object recognition with unknown geometric parameters. IEEE Trans. Image Process. 11(12), 1385–1396 (2002)

    Article  MathSciNet  Google Scholar 

  7. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)

    Article  Google Scholar 

  8. Fu, A.W., Keogh, E.J., Lau, L.Y.H., Ratanamahatana, C.A., Wong, R.C.: Scaling and time warping in time series querying. VLDB J. 17(4), 899–921 (2008)

    Article  Google Scholar 

  9. Gharavi-Alkhansari, M.: A fast globally optimal algorithm for template matching using low-resolution pruning. IEEE Trans. Image Process. 10(4), 526–533 (2001)

    Article  MATH  Google Scholar 

  10. Hel-Or, Y., Hel-Or, H.: Real-time pattern matching using projection kernels. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1430–1445 (2005)

    Article  Google Scholar 

  11. Ho, C., Agrawal, R., Megiddo, N., Srikant, R.: Range queries in OLAP data cubes. In: SIGMOD, pp. 73–88 (1997)

    Google Scholar 

  12. Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC, pp. 604–613 (1998)

    Google Scholar 

  13. Jagadish, H.V., Ooi, B.C., Tan, K., Yu, C., Zhang, R.: idistance: an adaptive b\({}^{\text{+ }}\)-tree based indexing method for nearest neighbor search. ACM Trans. Database Syst. 30(2), 364–397 (2005)

    Article  Google Scholar 

  14. Keim, D.A., Bustos, B.: Similarity search in multimedia databases. In: ICDE, p. 873 (2004)

    Google Scholar 

  15. Korn, F., Sidiropoulos, N., Faloutsos, C., Siegel, E.L., Protopapas, Z.: Fast nearest neighbor search in medical image databases. In: VLDB, pp. 215–226 (1996)

    Google Scholar 

  16. Kriegel, H.-P., Kröger, P., Kunath, P., Renz, M.: Generalizing the optimality of multi-step k-nearest neighbor query processing. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 75–92. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  17. Moshe, Y., Hel-Or, H.: Video block motion estimation based on gray-code kernels. IEEE Trans. Image Process. 18(10), 2243–2254 (2009)

    Article  MathSciNet  Google Scholar 

  18. Ouyang, W., Cham, W.: Fast algorithm for walsh hadamard transform on sliding windows. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 165–171 (2010)

    Article  Google Scholar 

  19. Ouyang, W., Tombari, F., Mattoccia, S., di Stefano, L., Cham, W.: Performance evaluation of full search equivalent pattern matching algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 127–143 (2012)

    Article  Google Scholar 

  20. Rakthanmanon, T., Campana, B.J.L., Mueen, A., Batista, G.E.A.P.A., Westover, M.B., Zhu, Q., Zakaria, J., Keogh, E.J.: Searching and mining trillions of time series subsequences under dynamic time warping. In: KDD, pp. 262–270 (2012)

    Google Scholar 

  21. Samet, H.: Techniques for similarity searching in multimedia databases. PVLDB 3(2), 1649–1650 (2010)

    Google Scholar 

  22. Schweitzer, H., Deng, R.A., Anderson, R.F.: A dual-bound algorithm for very fast and exact template matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 459–470 (2011)

    Article  Google Scholar 

  23. Seidl, T., Kriegel, H.: Optimal multi-step k-nearest neighbor search. In: SIGMOD, pp. 154–165 (1998)

    Google Scholar 

  24. Tao, Y., Yi, K., Sheng, C., Kalnis, P.: Quality and efficiency in high dimensional nearest neighbor search. In: SIGMOD, pp. 563–576 (2009)

    Google Scholar 

  25. Tombari, F., Mattoccia, S., di Stefano, L.: Full-search-equivalent pattern matching with incremental dissimilarity approximations. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 129–141 (2009)

    Article  Google Scholar 

  26. Viola, P.A., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  27. Weber, R., Schek, H., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: VLDB, pp. 194–205 (1998)

    Google Scholar 

  28. Yi, B., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: VLDB, pp. 385–394 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Tsz Nam Chan & Man Lung Yiu

  2. College of Engineering and Computer Science, University of Central Florida, Orlando, USA

    Kien A. Hua

Authors
  1. Tsz Nam Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Man Lung Yiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kien A. Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tsz Nam Chan .

Editor information

Editors and Affiliations

  1. Naval Academy Research Institute, Brest naval, France

    Christophe Claramunt

  2. University of Florida, Gainesville, Florida, USA

    Markus Schneider

  3. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Raymond Chi-Wing Wong

  4. Emory University, Atlanta, USA

    Li Xiong

  5. Gachon University, Gyeonggi-do, Korea (Republic of)

    Woong-Kee Loh

  6. University of Southern California, Los Angeles, USA

    Cyrus Shahabi

  7. Department of Computer Science, Pusan National University, Pusan, Korea (Republic of)

    Ki-Joune Li

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Chan, T.N., Yiu, M.L., Hua, K.A. (2015). A Progressive Approach for Similarity Search on Matrix. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22363-6_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22362-9

  • Online ISBN: 978-3-319-22363-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation