NDLI: Extraction of query term-related visual phrases for news video retrieval using mutual information

Content Provider	IEEE Xplore Digital Library
Author	Jun-Bin Yeh Chung-Hsien Wu
Copyright Year	2009
Description	Author affiliation: Dept. of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan (Jun-Bin Yeh; Chung-Hsien Wu)
Abstract	This paper presents an approach to query term-related visual phrases extraction using mutual information for object-based news video retrieval. As visual words are useful for object representation, unstable visual words generally appear in the frame sequence of a shot. Using the appearance frequency of the visual words in a sliding window over the query term-related stories, the appearance pattern of a visual word is adopted to characterize the visual word. Based on the appearance pattern of a visual word, the mutual information between two visual words can be estimated over all of the extracted stories. The mutual information is then used to construct a visual word relation graph. Visual phrases are then extracted by discovering the complete sub-graphs from the visual word relation graph for news video retrieval. Experiments were conducted on the MATBN news video database and the experimental results show that a good precision rate for video news retrieval can be achieved.
Starting Page	730
Ending Page	733
File Size	893829
Page Count	4
File Format	PDF
ISBN	9781424438273
DOI	10.1109/ISCAS.2009.5117852
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Information retrieval Mutual information Frequency Cameras Computer science Visual databases Solids Object recognition Robustness
Content Type	Text
Resource Type	Article

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