Mining recurrent items in multimedia with progressive resolution refinement

Osmar R. Zaiane, Jiawei Han, Hua Zhu

Research output: Contribution to journalArticlepeer-review


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 item-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.

Original languageEnglish (US)
Pages (from-to)461-470
Number of pages10
JournalProceedings - International Conference on Data Engineering
StatePublished - 2000
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Engineering(all)
  • Engineering (miscellaneous)


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