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  1. Proceedings of the 3rd international workshop on Automated information extraction in media production (AIEMPro '10)
  2. Role-based identity recognition for telecasts
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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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Role-based identity recognition for telecasts

Content Provider ACM Digital Library
Author Ndjiki-Nya, Patrick Riegel, Thomas Hutter, Andreas Petersohn, Christian Schwarze, Tobias Wirth, Stephan Han, Seunghan
Abstract Semantic queries involving image understanding aspects require the exploitation of multiple clues, namely the (inter-)relations between objects and events across multiple images, the situational context, and the application context. A prominent example for such queries is the identification of individuals in video sequences. Straightforward face recognition approaches require a model of the persons in question and tend to fail in ill conditioned environments. Therefore, an alternative approach is to involve contextual conditions of observations in order to determine the role a person plays in the current context. Due to the strong relation between roles, persons and their identities, knowing either often allows inferring about the other. This paper presents a system that implements this approach: First, robust face detection localizes the actors in the video. By clustering similar face instances the relative frequency of their appearance within a sequence is determined. In combination with a coarse textual annotation manually created by the broadcast station's archivist the roles and consequently the identities can be assigned and labeled in the video. Starting with unambiguous assignments and cascading appropriately most of the persons can be identified and labeled successfully. The feasibility and performance of the role-based person identification is demonstrated on basis of several programs of a popular German TV show, which consists of various elements like interview scenes, games and musical show acts.
Starting Page 27
Ending Page 32
Page Count 6
File Format PDF
ISBN 9781450301640
DOI 10.1145/1877850.1877859
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-10-29
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Face localization Searching Temporal segmentation Television programs Metadata Clustering Identity recognition Shot detection
Content Type Text
Resource Type Article
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