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 language||English (US)|
|Number of pages||10|
|Journal||Proceedings - International Conference on Data Engineering|
|State||Published - 2000|
ASJC Scopus subject areas
- Engineering (miscellaneous)