By Topic

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

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

2 Author(s)
Jun-Bin Yeh ; Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan ; Chung-Hsien Wu

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.

Published in:

Circuits and Systems, 2009. ISCAS 2009. IEEE International Symposium on

Date of Conference:

24-27 May 2009