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Noise Control Boundary Image Matching Using Time-Series Moving Average Transform  

Kim, Bum-Soo (강원대학교 컴퓨터과학과)
Moon, Yang-Sae (강원대학교 컴퓨터학부 컴퓨터과학전공)
Kim, Jin-Ho (강원대학교 컴퓨터학부 컴퓨터과학전공)
Publication Information
Journal of KIISE:Databases / v.36, no.4, 2009 , pp. 327-340 More about this Journal
Abstract
To achieve the noise reduction effect in boundary image matching, we use the moving average transform of time-series matching. Our motivation is based on an intuition that using the moving average transform we may exploit the noise reduction effect in boundary image matching as in time-series matching. To confirm this simple intuition, we first propose $\kappa$-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our $\kappa$-order image matching identifies similar images in this time-series domain by comparing the $\kappa$-moving average transformed sequences. Next, we propose an index-based matching method that efficiently performs $\kappa$-order image matching on a large volume of image databases, and formally prove the correctness of the index-based method. Moreover, we formally analyze the relationship between an order $\kappa$ and its matching result, and present a systematic way of controlling the noise reduction effect by changing the order $\kappa$. Experimental results show that our $\kappa$-order image matching exploits the noise reduction effect, and our index-based matching method outperforms the sequential scan by one or two orders of magnitude.
Keywords
Time-series databases; data mining; boundary image matching; time-series matching; moving average transform;
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1 Faloutsos, C., Ranganathan, M., and Manolopoulos, Y., 'Fast Subsequence Matching in Time-Series Databases,' In Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Minneapolis, Minnesota, pp.419-429, May 1994   DOI
2 Rafiei, D. and Mendelzon, A. O., 'Querying Time Series Data Based on Similarity,' IEEE Trans. on Knowledge and Data Engineering, vol.12, no.5, pp.675-693, Sept. 2000   DOI   ScienceOn
3 Keogh, E., 'Exact Indexing of Dynamic Time Warping,' In Proc. the 28th Int'l Conf. on Very Large Data Bases, Hong Kong, pp.406-417, Aug. 2002
4 Chatfield, C., The Analysis of Time Series: An Introduction, 3rd Ed., Chapman and Hall, 1984
5 Han, W.-S., Lee, J., Moon, Y.-S., and Jiang, H., 'Ranked Subsequence Matching in Time-Series Databases,' In Proc. the 33rd Int'l Conf. on Very Large Data Bases, Vienna, Austria, pp.423-434, Sept. 2007
6 Beckmann, N., Kriegel, H.-P., Schneider, R., and Seeger, B., 'The R$^{*}$ -tree: An Efficient and Robust Access Method for Points and Rectangles,' In Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Atlantic City, New Jersey, pp.322-331, May 1990   DOI
7 Rosenfeld, A. and Kalk, A. C, Digital Picture Processing, Vol. 1/2, 2nd Ed., Academic Press, New York, 1982
8 Gonzalez, R. C. and Woods, R. E., Digital Image Processing, 2nd Ed., Prentice Hall, New Jersey, 2002
9 Vlachos, M., Vagena, Z., Yu, P. S., and Athitsos, V., 'Rotation Invariant Indexing of Shapes and Line Drawings,' In Proc. of ACM Conf. on Information and Knowledge Management, Bremen, Germany, pp.131-138, Oct. 2005   DOI
10 Brailean, J. C. et al., 'Noise Reduction Filters for Dynamic Image Sequences: A Review,' In Proceedings of the IEEE, vol.83, no.9, Sept. 1995   DOI   ScienceOn
11 Moon, Y.-S. and Kim, J., 'Efficient Moving Average Transform-Based Subsequence Matching Algorithms in Time-Series Databases,' Information Sciences, vol.177, no.23, pp.5415-5431, Dec. 2007   DOI   ScienceOn
12 Agrawal, R., Faloutsos, C., and Swami, A., 'Efficient Similarity Search in Sequence Databases,' In Proc. the 4th Int'l Conf. on Foundations of Data Organization and Algorithms, Chicago, Illinois, pp. 69-84, Oct. 1993   DOI   ScienceOn
13 Keogh, E. and Ratanamahatana, C. A., 'Indexing and Mining Large Time Series Databases,' In Proc. The 12th Int'l Conf. on Database Systems for Advanced Applications, Tutorial, Bangkok, Thailand, Apr. 2007
14 Keogh, E., Wei, L., Xi, X., Lee, S.-H., and Vlachos, M., 'LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures,' In Proc. the 32rd Int'l Conf. on Very Large Data Bases, Seoul, Korea, pp.882-893, Sept. 2006
15 Moon, Y.-S., Whang, K.-Y., and Han, W.-S., 'General Match: A Subsequence Matching Method in Time-Series Databases Based on Generalized Windows,' In Proc. Int'l Conf. on Management of Data, ACM SIGMOD, Madison, Wisconsin, pp. 382-393, June 2002   DOI
16 Loh, W.-K., Park, Y.-H., and Yoon, Y-I., 'Fast Recognition of Asian Characters Based on Database Methodologies,' In Proc. the 24th British Nat'. Conf. on Databases, Glasgow, UK, pp.37-48, July 2007   DOI   ScienceOn
17 Lee, A. J. T. et al., 'A Novel Filtration Method in Biological Sequence Databases,' Pattern Recognition Letters, vol.28, Issue 4, pp.447-458, Mar. 2007   DOI   ScienceOn
18 Do, M. N., 'Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback -Leibler Distance,' IEEE Trans. on Image Processing, vol.11, no.2, Feb. 2002   DOI   ScienceOn
19 Loh, W.-K., Kim, S.-W., and Whang, K.-Y., 'Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases,' IEICE Transactions on Information and Systems, vol.E84-D, no.1, pp.76-86, 2000
20 Zhang, D. Z. and Lu, G., 'Review of Shape Representation and Description Techniques,' Pattern Recognition, vol.37, no.1, pp.1-19, July 2003   DOI   ScienceOn
21 Theoharatos, C., 'A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test,' IEEE Trans. on Knowledge and Data Engineering, vol.17, no.6, pp.808-819, June 2005   DOI   ScienceOn