Finding Motifs in a Database of Shapes

Proceedings of the 7th SIAM International Conference on Data Mining

Volumn , Issue , 2007, Pages 249-260

  Xi, Xiaopeng ^a   Keogh, Eamonn ^a   Wei, Li ^a   Mafra Neto, Agenor ^b  

^a University of California ^*  (United States)

^b ISCA Technologies   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; LARGE DATASET;

BRUTE FORCE SEARCH; DISTANCE MEASURE; DIVERSE DOMAINS; IMAGE COLLECTIONS; REAL-WORLD DATASETS; ROTATION INVARIANT; TARGET IMAGES; THREE ORDERS OF MAGNITUDE;

DATA MINING;

EID: 70449088820     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972771.23     Document Type: Conference Paper

References (44)

1. Adamek,T. and O'Connor, N.E. A multiscale representation method for nonrigid shapes with a single closed contour. IEEE Circuits and Systems for Video Technology,14(5): 742-753,2004.
2. Adamek, T. and O'Connor, N.E. Efficient contour-based shape representation and matching. Multimedia Information Retrieval 2003:138-143.
3. Attalla, E. and Siy, P. Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching. Pattern Recognition,38(12): 2229-2241,2005.
4. Berrani, S., Amsaleg, L., and Gros, P. Robust content-based image searches for copyright protection. In Proceedings of ACM Workshop on Multimedia Databases,2003.
5. Cardone, A., Gupta, S.K., and Karnik, M. A survey of shape similarity assessment algorithms for product design and manufacturing applications. ASME Journal of Computing and Information Science in Engineering,3(2): 109-118,2003.
6. Chan, K. and Fu, A. W. Efficient time series matching by wavelets. In Proceedings of the IS IEEE International Conference on Data Engineering,pp. 126-133, 1999.
7. Chang, E., Wang, J, Li, C, and Wiederhold, G. RIME: A replicated image detector for the world-wide web. In Proceedings of SPIE,1998.
8. Chiu, B., Keogh, E., and Lonardi, S. Probabilistic discovery of time series motifs. In Proceedings of $ International Conference on Knowledge Discovery and Data Mining (SIGKDD W),pp 493-498,2004.
9. Cuviers, G. Le Regne Animal distribue d'apres son Organisation pour servir de Base a l'Histoire Naturelle des Animeaux et d'Introduction a l'Anatomie combaree. Printed by Paul Renouard, published by Fortin, Masson et Cie, Libraires, in Paris/France, 1839.
10. Evans, J. E. and Maunder, E. W. Experiments as to the Actuality of the 'Canals' observed on Mars. MNRAS,63 (1903)488.
11. Fridrich, J., Soukal, D., and Lukas, J. Detection of copy-move forgery in digital images. In Digital Forensic Research Workshop,2003.
12. Gdalyahu, Y. and Weinshall, D. Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI'99), 21(12): 1312-1328, Dec. 1999.
13. Gutierrez, A. www.graffititracker.net, 2006.
14. Hall, D, A. and Wisner, G. Texas site suggests link with European Upper Paleolithic. Mammoth Trumpet (Journal of the Center for the Study of the First Americans)Volume 15, Number 1,2000.
15. Joron, M. Mimicry. In Resh V. and Carde, RT (eds) Encyclopedia of Insects.Elsevier Science, San Diego CA,pp 714-726,2003.
16. Ke, Y., Sukthankar, R., and Huston, L. Efficient near-duplicate detection and sub-image retrieval. ACM Multimedia Conference,2004.
17. Keogh, E., Chakrabarti, K., Pazzani, M, and Mehrotra.Dimensionality reduction for fast similarity search in large time series databases.Journal of Knowledge and Information Systems,pp 263-286, 2000.
18. Keogh, E. and Kasetty, S. On the need for time series data mining benchmarks: a survey and empirical demonstration. In Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,pp 102-111,2002.
19. Keogh, E., Lonardi, S., and Ratanamahatana, C.A. Towards parameter-free data mining. In Proceedings of 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2004.
20. 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 Proceedings of Very Large Databases(VLDB-06),2006, pp 882-893.
21. Kephart, T. Graffiti as Intelligence for Law Enforcement. Western Society of Criminology 31s' Annual Conference, February 19-22,2004.
22. Kitaguchi, S. Extracting Feature based on Motif from a Chronic Hepatitis Dataset. In Proceedings of 18th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI'04),2004.
23. Latecki, L.J., Lakaemper, R. and Eckhardt, U. Shape Descriptors for Non-rigid Shapes with a Single Closed Contour. CVPR, pp. 424-429,2000.
24. LeCun, Y., Bottou, L., Bengio, Y. and Haffher, P. Gradient -based learning applied to document recognition. Proceedings of the IEEE,86(11): 2278-2324, 1998.
25. Li, D. and Simske, S. Shape retrieval based on distance ratio distributions. HP Tech Report. HPL-2002-251.
26. Lin, J., Keogh, E., Lonardi, S., and Chiu, B. A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In Proceeding of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,2003.
27. Lin,J., Keogh, E., Lonardi, S., Lankford, J.P.,and Nystrom, D.M. Visually Mining and Monitoring Massive Time Series. SIGKDD,pp 460-469,2004.
28. Mokhtarian, F.and Bober, M. Curvature Scale Space Representation: Theory, Applications and MPEG-7 Standardization. Kluwer Academic,2003.
29. Mollineda, R. A., Vidal, E., and Casacuberta, F. Cyclic Sequence Alignments: Approximate Versus Optimal Techniques. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI),16(3): 291-299, 2002.
30. Osada, R., Funkhouser, T., Chazelle, B., and Dobkin, D. Shape Distributions.ACM Transactions on Graphics, 21(4): 807-832, October, 2002.
31. Pan,J.Y., Balan,A., Xing, P.,Traina,A.J.M., and Faloutsos,C. Automatic Mining of Fruit Fly Embryo Images. SIGKDD,2006, to appear.
32. Pope, G. A. Weathering of petroglyphs: direct assessment and implications for dating methods. Antiquity,74(2000): 833-843.
33. Ratanamahatana, C.A. and Keogh, E. Three myths about Dynamic Time Warping Data Mining. In Proceedings of SIAM International Conference on Data Mining (SDM'05),Newport Beach, CA, 2005.
34. Richtsmeier J, DeLeon V, Lele S. The Promise of Geometric Morphometrics. American Journal of Physical Anthropology. Vol. 119 iss:s35 pp:63 -91, 2002.
35. Rombo, S. and Terracina, G. Discovering Representative Models in Large Time Series Databases. In Proceedings of International Conference on Flexible Query Answering Systems,pp 84-97, 2004.
36. Said, C. Revolutionary chapter: Google's ambitious book-scanning plan seen as key shift in paper-based culture. San Francisco Chronicle. December 20,2004.
37. Tompa, M. & Buhler, J. Finding motifs using random projections. In proceedings of the 5' Int'l Conference on Computational Molecular Biology.Montreal, Canada, Apr 22-25. pp 67-74, 2001.
38. Wang, Z., Chi, Z., Feng, D., and Wang, Q. Leaf Image Retrieval with Shape Features. In Proceedings of the 4th International Conference on Advances in Visual Information Systems,pp 477- 487, 2000.
39. Watchman, A. A universal standard for reporting the ages of petroglyphs and rock paintings. In M. Strecker and P.Bahn (eds), Dating and the earliest known rock art,pp. 1 -3. Oxbow Books, Oxford, 1999.
40. West, W. and West, G.S. A Monograph of the British Desmidiaceae. Vols.I-V. The Ray Society,London, 1904-1922.
41. www.cs.ucr.edu/~xxi/SDM07.
42. Zhang, D. and Lu, G. Review of shape representation and description techniques. Pattern Recognition,37(1):1-19,2004.
43. Zhang, D. and Chang, S. Detecting image near-duplicate by stochastic attributed relational graph matching with learning. ACM Multimedia 2004: 877-884.
44. Zunic, J., Rosin, p., and Kopanja, L. Shape Orientability. ACCV (2) 2006: pp 11-20.