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  1. Proceedings of the 3rd international workshop on Automated information extraction in media production (AIEMPro '10)
  2. Unsupervised event segmentation of news content with multimodal cues
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Technologies for next-generation multi-media libraries: the contentus project
Narrative theme navigation for sitcoms supported by fan-generated scripts
Towards automatic speaker retrieval for large multimedia archives
Implicit news recommendation based on user interest models and multimodal content analysis
Generic architecture for event detection in broadcast sports video
A novel video thumbnail extraction method using spatiotemporal vector quantization
Content-based video genre classification using multiple cues
Unsupervised event segmentation of news content with multimodal cues
Shiatsu: semantic-based hierarchical automatic tagging of videos by segmentation using cuts
Role-based identity recognition for telecasts
Automatic news recommendations via profiling
Efficient video breakup detection and verification

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Unsupervised event segmentation of news content with multimodal cues

Content Provider ACM Digital Library
Author Broilo, Mattia Basso, Andrea Zavesky, Eric De Natale, Francesco G. B.
Abstract In the age of content snacking and mobisodes (mobile episodes) the paradigm of media consumption is radically changing. Media consumption is moving from monolithic, prepackaged, well-edited, and elaborate content presentation to a continuous feed of brief segments as singleton episodes and few-minutes videos, that are often supported by or initiated via tweets and status updates. In these updates, attention spans are small and the content packaging is less relevant with respect to the dynamic, 'streaming' aspect of information. This trend has a profound influence on the segmentation requirements that are needed to make this stream of possible information. In this paper, we present a novel method to automatically extract structured content, events, where events include major cast interviews, dialogs, background segments, etc. from news video in an unsupervised fashion. Two key ideas differentiate this unsupervised method from the others: the type of information that we use to find events and the method utilized to combine this information for coherent multimedia events. The proposed system exploits audio, visual appearance, detected faces, and mid-level semantic concepts from every video shot, but instead of combining everything together, the framework clusters them independently and by applying coherence rules assembles the multimedia events. Additionally, we discuss the effect of segmentation errors in practical retrieval and content consumption tasks.
Starting Page 39
Ending Page 44
Page Count 6
File Format PDF
ISBN 9781450301640
DOI 10.1145/1877850.1877862
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-10-29
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Content-based multimedia indexing Video events Unsupervised clustering
Content Type Text
Resource Type Article
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