Massive Real-Time Data Mining Algorithm for a Multimedia Database

Authors

  • Jiaju Gong Electronic and Information Technology, Jiangmen Polytechnic, Jiangmen 529030, China
  • Qin Wu Electronic and Information Technology, Jiangmen Polytechnic, Jiangmen 529030, China

Abstract

The traditional classification mining algorithm for massive data has a complicated calculation process, poor real-time performance and low accuracy. This paper presents a real-time data mining algorithm for a multimedia database. The
wavelet de-noising method is used to de-noise the data in the multimedia database to reduce the interference of the
background area to the feature extraction of the multimedia data. The effective area extraction algorithm based on the
center of mass is used to extract the effective area and is combined with the SIFT algorithm and LBP algorithm to extract
the data features from the multimedia database. Experimental results show that the algorithm is accurate, reliable, has
a high real-time mining ability and a practical ability.

Keywords: Multimedia; Database; Massive; Real-time; Data Classification; Mining

Cite As

J. Gong and Q. Wu, "Massive Real-Time Data Mining Algorithm for a Multimedia Database",
Engineering Intelligent Systems, vol. 30 no. 1, pp. 35-37, 2022.




Downloads

Published

2022-01-01

Issue

Vol. 30 No. 1 (2022): International Journal of Engineering Intelligent Systems

Section

General Submission

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.