By Topic

Mining recurrent items in multimedia with progressive resolution refinement

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zaiane, O.R. ; Dept. of Comput. Sci., Alberta Univ., Edmonton, Alta., Canada ; Jiawei Han ; Zhu, H.

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

Published in:

Data Engineering, 2000. Proceedings. 16th International Conference on

Date of Conference: