NDLI: A general Framework of video segmentation to logical unit based on conditional random fields

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A general Framework of video segmentation to logical unit based on conditional random fields

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A general Framework of video segmentation to logical unit based on conditional random fields

Content Provider	ACM Digital Library
Author	Xu, Su Feng, Bailan Chen, Zhineng Xu, Bo
Abstract	Segmenting video into logical units like scenes in movies and topic units in News videos is an essential prerequisite for a wide range of video related applications. In this paper, a novel approach for logical unit segmentation based on conditional random fields (CRFs) is presented. In comparison with previous approaches that handle scenes and topic units separately, the proposed approach deals with them in a general framework. Specifically, four types of shots are defined and represented by four middle-level features, i.e., shot difference, scene transition, shot theme and audio type. Then, the problem of logical unit segmentation is novelly formulated as a problem of identifying the type of shot based on the extracted features, by leveraging the CRFs model. The proposed framework effectively integrate visual, audio and contextual features, and it is able to produce ideal result for both scene and topic unit segmentation. The effectiveness of the proposed approach is verified on seven mainstream types of videos, from which average F-measures of 88% and 86% on scenes and topic units are reported respectively, illustrating that the proposed method can accurately segment logical units in different genres of videos.
Starting Page	247
Ending Page	254
Page Count	8
File Format	PDF
ISBN	9781450320337
DOI	10.1145/2461466.2461506
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Conditional random field Topic unit segmentation Scene segmentation
Content Type	Text
Resource Type	Article

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