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Efficient and effective state-based framework for news video retrieval

Efficient and effective state-based framework for news video retrieval

Zheng, Aihua, Ma, Jixin, Zhou, Xiaoyi and Luo, Bin (2010) Efficient and effective state-based framework for news video retrieval. International Journal of Advancements in Computing Technology, 2 (4). pp. 151-161. ISSN 2005-8039 (Print), 2233-9337 (Online) (doi:10.4156/ijact.vol2.issue4.16)

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Abstract

In this paper, an efficient and effective framework is proposed for news video retrieval. Firstly, the 64-dimensional colour histogram is extracted as the feature vector. Then the pair quantizer is adopted to transfer the news video retrieval problem into multi-dimensional string matching problem, which conduces to the efficiency to the framework. Secondly, a new measurement named ‘optimal temporal common subsequence’, which distinguishes the difference caused by rich temporal characteristics including temporal order, temporal duration and temporal gap, is designed to match state-sequence, followed by the point & interval-based formal characterization of time-series and state-sequences. Thirdly, we tested the proposed measurement on news video retrieval. The performance shows the proposed algorithm is more effective for news video retrieval.

Item Type: Article
Uncontrolled Keywords: state-sequence matching, optimal temporal common subsequence, news video retrieval
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Computer Science
Related URLs:
Last Modified: 14 Oct 2016 09:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/4319

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