Index-based Boundary Matching Supporting Partial Denoising for Large Image Databases

Bum-Soo Kim

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2019, 24(10), pp.91-99
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Bum-Soo Kim ¹

¹한국건설기술연구원

ABSTRACT

In this paper, we propose partial denoising boundary matching based on an index for faster matching in very large image databases. Attempts have recently been made to convert boundary images to time-series with the objective of solving the partial denoising problem in boundary matching. In this paper, we deal with the disk I/O overhead problem of boundary matching to support partial denoising in a large image database. Although the solution to the problem superficially appears trivial as it only applies indexing techniques to boundary matching, it is not trivial since multiple indexes are required for every possible denoising parameters. Our solution is an efficient index-based approach to partial denoising using R*-tree in boundary matching. The results of experiments conducted show that our index-based matching methods improve search performance by orders of magnitude.
KEYWORDS

Boundary matching, Partial denoising, Time-series data, Time-series matching, Indexing
REFERENCES (29)

[confproc] R. Agrawal / 1993 / Efficient Similarity Search in Sequence Databases / Proc. of the 4th Int'l Conf. on Foundations of Data Organization and Algorithms : 69~84

[confproc] Y. Cai / 2015 / Facets : Fast Comprehensive Mining of Coevolving High-Order Time Series / Proc. of the 21th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining : 79~88

[journal] W. -K. Loh / 2015 / Envelope-based Boundary Image Matching for Smart Devices under Arbitrary Rotations / Multimedia Systems 21(1) : 29~47

[journal] Y. -S. Moon / 2014 / Safe MBR-Transformation in Similar Sequence Matching / Information Sciences 270(2) : 28~40

[confproc] J. Paparrizos / 2015 / k-Shape : Efficient and Accurate Clustering of Time Series / Proc. of the ACM SIGMOD Int'l Conf. on Management of Data : 1855~1870

[journal] B. -S. Kim / 2017 / Boundary Image Matching Supporting Partial Denoising Using Time-Series Matching Techniques / Multimedia Tools and Applications 76(6) : 8471~8496

[journal] Y. -S. Moon / 2015 / Triangular Inequality-based Rotation-Invariant Boundary Image Matching for Smart Devices / Multimedia Systems 21(1) : 15~28

[journal] A. Dalton / 2012 / Development of a Body Sensor Network to Detect Motor Patterns of Epileptic Seizures / IEEE Trans. on Biomedical Engineering 59(11) : 3204~3211

[journal] M. Bashir / 2012 / Advanced Biometric Pen System for Recording and Analyzing Handwriting / Journal of Signal Processing Systems 68(1) : 75~81

[journal] B. -S. Kim / 2014 / Interactive Noise-Controlled Boundary Image Matching Using the Time-Series Moving Average Transform / Multimedia Tools and Applications 72(3) : 2543~2571

[journal] Y. -S. Moon / 2010 / Scaling-Invariant Boundary Image Matching Using Time-Series Matching Techniques / Data & Knowledge Engineering 69(10) : 1022~1042

[confproc] F. Korn / 1997 / Efficiently Supporting Ad Hoc Queries in Large Data Sets of Time Sequences / Proc. of the ACM SIGMOD Int'l Conf. on Management of Data : 289~300

[journal] R. Datta / 2008 / Image Retrieval : Ideas, Influences, and Trends of the New Age / ACM Computing Surveys 40(2) : 34~94

[journal] A. W. M. Smeulders / 2000 / Content-based Image Retrieval at the End of the Early Years / IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12) : 1349~1380

[confproc] N. Beckmann / 1990 / The R*-tree : An Efficient and Robust Access Method for Points and Rectangles / Proc. of the ACM SIGMOD Int’l Conf. on Management of Data : 322~331

[confproc] C. Faloutsos / 1994 / Fast Subsequence Matching in Time-Series Databases / Proc. of the ACM SIGMOD Int’l Conf. on Management of Data : 419~429

[confproc] W. -S. Han / 2011 / A New Approach for Processing Ranked Subsequence Matching based on Ranked Union / Proc. of the ACM SIGMOD Int'l Conf. on Management of Data : 457~468

[confproc] M. Vlachos / 2002 / Discovering Similar Multidimensional Trajectories / Proc. of the 18th IEEE Int'l Conf. on Data Engineering : 673~684

[confproc] L. Chen / 2005 / Robust and Fast Similarity Search for Moving Object Trajectories / Proc. of the ACM SIGMOD Int'l Conf. on Management of Data : 491~502

[book] R. H. Shumway / 2006 / Time Series Analysis and Its Applications: With R Examples / Springer Texts in Statistics

[confproc] S. Berchtold / 1998 / The Pyramid-Technique : Towards Breaking the Curse of Dimensionality / Proc. of the ACM SIGMOD Int’l Conf. on Management of Data : 142~153

[journal] K. -P. Chan / 2003 / Haar Wavelets for Efficient Similarity Search of Time-Series : With and Without Time Warping / IEEE Trans. on Knowledge and Data Engineering 15(3) : 686~705

[confproc] R. Lesch / 1999 / Component Analysis in Financial Time Series / Proc. of the IEEE Int'l Conf. on Computational Intelligence for Financial Engineering : 183~190

[journal] M. Krawczak / 2014 / An Approach to Dimensionality Reduction in Time Series / Information Sciences 260(2) : 15~36

[journal] S. Belongie / 2002 / Shape Matching and Object Recognition Using Shape Contexts / IEEE Trans. on Pattern Analysis and Machine Intelligence 24(4) : 509~522

[book] M. Sonka / 2014 / Image Processing, Analysis, and Machine Vision / Cengage Learning

[journal] P. Suetens / 1992 / Computational Strategies for Object Recognition / ACM Computing Surveys 24(1) : 5~62

[journal] Y. -S. Moon / 2007 / Efficient Moving Average Transform-based Subsequence Matching Algorithms in Time-Series Databases / Information Sciences 177(23) : 5415~5431

[confproc] R. Arandjelovi´c / 2012 / Three Things Everyone Should Know to Improve Object Retrieval / Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition : 2911~2918
KCI Citation (0)

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