NDLI: A new rule-based video classification approach

Content Provider	IEEE Xplore Digital Library
Author	Ye Yuan Jun-Yi Shen Qin-Bao Song
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Xi'an Jiaotong Univ., China (Ye Yuan; Jun-Yi Shen; Qin-Bao Song)
Abstract	This paper addresses the problem of lower precision in automatic video classification. A novel rule-based video classification approach is proposed. Firstly, in the video segmentation process, a set of video attributes is extracted to represent the content of video and a video attribute database is generated. Then the decision tree and class association rule mining techniques are performed on this video attribute database to extract a decision tree rule set and a class association rule set respectively. Lastly, a combination and pruning algorithm are applied to combine these two rule sets to generate a final classification rule set. The experimental result verifies the consistency of decision tree classification with class association classification. The result also shows the final combined rule set has higher classification precision that just one rule set.
Sponsorship	IEEE Systems Man and Cybernetics Tech. Committee on Cybernetics, Hebei Univ. Northwestern Polytechnical Univ
Starting Page	225
Ending Page	230
File Size	644401
Page Count	6
File Format	PDF
ISBN	0780381319
DOI	10.1109/ICMLC.2003.1264476
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification tree analysis Decision trees Association rules Videoconference Data mining Video compression Databases Indexing Computer science Electronic mail
Content Type	Text
Resource Type	Article

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