Mining Recurrent Items in Multimedia with Progressive Resolution Refinement
- Osmar R. Zaiane, University of Alberta (Database)
- Jiawei Han
- Hua Zhu
Despite the overwhelming amounts of multimedia data recently generated and the significance of such data, very few people have systematically investigated multimedia data mining. With our previous studies on content-based retrieval of visual artifacts, we study in this paper the methods for mining content-based associations with recurrent items and with spatial relationships from large visual data repositories. A progressive resolution refinement approach is proposed in which frequent item-sets at rough resolution levels are mined, and progressively, finer resolutions are mined only on the candidate frequent items-sets derived from mining rough resolution levels. Such a multi-resolution mining strategy substantially reduces the overall data mining cost without loss of the quality and completeness of the results.
Citation
O. Zaiane, J. Han, H. Zhu. "Mining Recurrent Items in Multimedia with Progressive Resolution Refinement". International Conference on Data Engineering (ICDE), San Diego, USA, pp 461-470, February 2000.| Keywords: | |
| Category: | In Conference |
| Web Links: | IEEE |
BibTeX
@incollection{Zaiane+al:ICDE00,
author = {Osmar R. Zaiane and Jiawei Han and Hua Zhu},
title = {Mining Recurrent Items in Multimedia with Progressive Resolution
Refinement},
Pages = {461-470},
booktitle = {International Conference on Data Engineering (ICDE)},
year = 2000,
}Last Updated: February 04, 2020Submitted by Sabina P
