Efficient sampling of training set in large and noisy multimedia data

ACM Transactions on Multimedia Computing, Communications and Applications

Volumn 3, Issue 3, 2007, Pages

  Wang, Surong ^a,b   Dash, Manoranjan ^a,b   Chia, Liang Tien ^a,b   Xu, Min ^a,b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Audio event identification; Histogram; Image classification; Noise; Sampling

Indexed keywords

DATA STORAGE EQUIPMENT; DATA STRUCTURES; IMAGE CLASSIFICATION; MULTIMEDIA SERVICES; SAMPLING; SIGNAL TO NOISE RATIO;

AUDIO EVENT IDENTIFICATION; HISTOGRAMS; SIMPLE RANDOM SAMPLE (SRS);

LEARNING SYSTEMS;

EID: 34548749801     PISSN: 15516857     EISSN: 15516865     Source Type: Journal    
DOI: 10.1145/1236471.1236473     Document Type: Article

References (40)

1. AGRAWAL, R., IMIELINSKI, T., AND SWAMI, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of the International Conference on Management of Data.
2. AGRAWAL, R. AND SRIKANT, R. 1994. Fast algorithms for mining association rules. In Proceedings of the International Conference on Very Large Databases.
3. ANGLUIN, D. 1988. Queries and concept learning. Mach. Learn. 2, 4, 319-342.
4. ASTASHYN, A. 2004. Deterministic data reduction methods for transactional data sets. Master thesis.
5. ATLAS, L., COHN, D., LADNER, R., EL-SHARKAWI, M. A., AND R. J. MARKS, I. 1990. Training connectionist networks with queries and selective sampling. In Advances in Neural Information Processing Systems, vol. 2. Morgan. Kaufmann, San Fransisco, CA. 566-573.
6. BREIMAN, L. 1996. Bagging predictors. Mach. Learn. 24, 2, 123-140.
7. BRODLEY, C. AND FRIEDL, M. 1999. Identifying mislabeled training data. J. Artif. Intell. Res. 11, 131-167.
8. BRONNIMANN, H., CHEN, B., DASH, M., HAAS, P., AND SCHEUERMANN, P. 2003. Efficient data reduction with EASE. In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining. 59-68.
9. CHAPELLE, O., HALFFINER, P., AND VAPNIK, V. N. 1999. Support vector machine for histogram based image classification. IEEE Trans. Neutral Netw. 10, 5, 1055-1064.
10. CHAWLA, N., ESCHRICH, S., AND HALL, L. O. 2001. Creating ensembles of classifiers. In Proceedings of the International Conference on Data Mining. 580-581.
11. CHEN, B., HAAS, P., AND SCHEUERMANN, P. 2002. A new two-phase sampling based algorithm for discovering association rules. In Proceedings of the International Conference on Knowledge Discovery and Data Mining.
12. COHN, D. A., GHAHRAMANI, Z., AND JORDAN, M. I. 1995. Active learning with statistical models. In Advances in Neural Information Processing Systems, vol. 7. MIT Press, New Yark. 705-712.
13. DOUGHERTY, J., KOHAVI, R., AND SAHAMI, M. 1995. Supervised and unsupervised discretization of continuous features. In International Conference on Machine Learning. 194-202.
14. DUAN, L., XU, M., CHUA, T., TIAN, Q., AND XU, C. 2003. A mid-level representation framework for semantic sports video analysis. In Proceedings of the ACM Multimedia Conference.
15. GU, B., F. HU, F., AND LIU, H. 2000. Sampling and its applications in data mining: A. survey. Tech. Rep. School of Computing, National University of Singapore, Singapore.
16. HAN, J., PEI, J., AND YIN, Y. 2000. Mining frequent patterns without candidate generation. In Proceedings of the International Conference on Management of Data.
17. ISO/IEC15938-8/FDIS3. IED. 2005. Information technology - Multimedia content description interface - Part 8: Extraction and use of MPEG-7 descriptions.
18. IYENGAR, V. S., APTE, C., AND ZHANG, T. 2000. Active learning using adaptive resampling. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. 92-98.
19. JIN, R., YAN, R., AND HAUPTMANN, A. 2003. Image classification using a bigram model. In AAAI Spring Symposium on Intelligent Multimedia Knowledge Management.
20. LEWIS, D. D. AND CATLETT, J. 1994. Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the 11th International Conference on Machine Learning, W. W. Cohen and H. Hirsh, eds. Morgan Kaufmann, San Francisco, CA. 148-156.
21. LEWIS, D. D. AND GALE, W A. 1994. A sequential algorithm for training text classifiers. In Proceedings of the SIGIR 17th ACM International Conference on Research and Development in Information Retrieval, W. B. Croft and C. J. van Rijsbergen, eds. Springer Verlag, Berlin. 3-12.
22. MANJUNATH, B. S., SALEMBIER, P., AND SIKORA, T. 2002. Introduction to MPEG-7. John Wiley, New Yark.
23. MEEK, C., THIESSON, B., AND HECKERMAN, D. 2002. The learning-curve sampling method applied to model-based clustering. J. Mach. Learn. Res. 2, 3, 397-418.
24. NEPAL, S., SRINIVASAN, U., AND REYNOLDS, G. 2001. Automatic detection of goal segments in basketball videos. In Proceedings of the ACM Multimedia Conference (Los Angeles, CA).
25. OJALA, T., AITTOLA, M., AND MATINMIKKO, E. 2002. Empirical evaluation MPEG-7 XM color descriptors in content-based retrieval of semantic image categories. In Proceedings of the 16th International Conference on Pattern Recognition (Quebec, Canada). 1021-1024.
26. OLKEN, F. 1993. Random sampling from databases. Ph.D. thesis, Department of Computer Science, University of California, Berkely.
27. PLUTOWSKI, M. AND WHITE, H. 1993. Selecting concise training sets from clean data. IEEE Trans. Neural Netw. 4, 2, 305-318.
28. RUI, Y., GUPTA, A., AND ACERO, A. 2000. Automatically extracting highlights for TV baseball programs. In Proceedings of the ACM Multimedia Conference. 105-115.
29. SAAR-TSECHANSKY, M. AND PROVOST, F. 2001. Active learning for class probability estimation and ranking. In Proceedings of the 17th International Joint Conference on Artificial Intelligence. 911-920.
30. SARAWAGI, S. AND BHAMIDIPATY, A. 2002. Interactive deduplication using active learning. In Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining.
31. SCHEFFER, T., DECOMAIN, C., AND WROBEL, S. 2001. Active hidden. Markov models for information extraction. In Proceedings of the International Symposium on Intelligent Data Analysis.
32. TONG, S. AND KOLLER, D. 2000. Support vector machine active learning with applications to text classification. In Proceedings of the 17th International Conference on Machine Learning, P. Langley, ed. Morgan Kaufmann, San Francisco, CA. 999-1006.
33. VITTER, J. 1985. Random sampling with a reservoir. ACM Trans. Math. Softw. 11, 1 (Mar.), 37-57.
34. WANG, S., DASH, M., AND CHIA, L.-T. 2005a. Efficient sampling for image application. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining.
35. WANG, S., XU, M., CHIA, L.-T., AND DASH, M. 2005b. Easier sampling for audio event identification. In Proceedings of the International Conference on Multimedia and Expo.
36. XU, M., DUAN, L.-Y., CAI, J., CHIA, L.-T., XU, C-S., AND TIAN, Q. 2004a. Hmm-Based audio keyword generation. In Proceedings of the Pacific Conference on Multimedia, vol. 3. 566-574.
37. XU, M., DUAN, L.-Y., CHIA, L.-T., AND C.-S.XU. 2004b. Audio keywords generation for sports video analysis. In Proceedings of the ACM Multimedia Conference.
38. YOUNG, S., EVERMAN, G., GALES, M., HAIN, T., KERSHAW, D. MOORE, G., ODELL, J., OLLASON, D., POVEY, D., VALTCHEV, V., AND WOODLAND, P. 2002. The HTK book (for HTK version 3.1). Cambridge University Engineering Department.
39. ZHU, X. AND WU, X. 2004. Class noise vs. attribute noise: A quantitative study of their impacts. Artif. Intell. Rev. 22, 3, 177-210.
40. ZHU, X., WU, X., AND CHEN, S. 2003. Eliminating class noise in large dataseis. In Proceedings of the 20th ICML International Conference on Machine Learning. 920-927.