Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Automatic Caption Localization in Compressed Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-Modal Approach to Story Segmentation for News Video
World Wide Web
Scene detection in videos using shot clustering and sequence alignment
IEEE Transactions on Multimedia
Video scene detection using graph-based representations
Image Communication
A unified scheme of shot boundary detection and anchor shot detection in news video story parsing
Multimedia Tools and Applications
Detection and representation of scenes in videos
IEEE Transactions on Multimedia
Video scene segmentation using Markov chain Monte Carlo
IEEE Transactions on Multimedia
A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams
IEEE Transactions on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
A Formal Study of Shot Boundary Detection
IEEE Transactions on Circuits and Systems for Video Technology
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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.