A Progressive Approach for Similarity Search on Matrix

  • Tsz Nam ChanEmail author
  • Man Lung Yiu
  • Kien A. Hua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


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.


Near Neighbor Query Image Multimedia Database Exact Distance Query Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Brad, R., Letia, I.A.: Extracting cloud motion from satellite image sequences. In: ICARCV, pp. 1303–1307 (2002)Google Scholar
  5. 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. 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)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefzbMATHGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 11.
    Ho, C., Agrawal, R., Megiddo, N., Srikant, R.: Range queries in OLAP data cubes. In: SIGMOD, pp. 73–88 (1997)Google Scholar
  12. 12.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC, pp. 604–613 (1998)Google Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Keim, D.A., Bustos, B.: Similarity search in multimedia databases. In: ICDE, p. 873 (2004)Google Scholar
  15. 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. 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) CrossRefGoogle Scholar
  17. 17.
    Moshe, Y., Hel-Or, H.: Video block motion estimation based on gray-code kernels. IEEE Trans. Image Process. 18(10), 2243–2254 (2009)MathSciNetCrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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. 21.
    Samet, H.: Techniques for similarity searching in multimedia databases. PVLDB 3(2), 1649–1650 (2010)Google Scholar
  22. 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)CrossRefGoogle Scholar
  23. 23.
    Seidl, T., Kriegel, H.: Optimal multi-step k-nearest neighbor search. In: SIGMOD, pp. 154–165 (1998)Google Scholar
  24. 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. 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)CrossRefGoogle Scholar
  26. 26.
    Viola, P.A., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  27. 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. 28.
    Yi, B., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: VLDB, pp. 385–394 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of ComputingHong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.College of Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

Personalised recommendations