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Canonical processes for creating personalized semantically rich multimedia presentations.

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Title: Canonical processes for creating personalized semantically rich multimedia presentations.
Authors: Scherp, Ansgar1 mail@ansgarscherp.net
Source: Multimedia Systems. Dec2008, Vol. 14 Issue 6, p415-425. 11p. 7 Diagrams, 1 Chart.
Subjects: Multimedia communications, Interactive multimedia authoring programs, Web personalization, Semantics, Digital images, Digital video, Digital audio
Abstract: Authoring of multimedia content can be considered as composing media assets such as images, videos, text, and audio in time, space, and interaction into a coherent multimedia presentation. Personalization of such content means that it reflects the users’ or user groups’ profile information and context information. Enriching the multimedia content with semantically rich metadata allows for a better search and retrieval of the content. To actually create personalized semantically-rich multimedia content, a manual authoring of the many different documents for all the different users’ and user groups’ needs is not feasible. Rather a (semi-)automatic authoring of the content seems reasonable. We have analyzed in detail today’s approaches and systems for authoring, personalizing, and semantically enriching multimedia presentations. Based on this analysis, we derived a general creation chain for the (semi-)automatic generation of such content. In this paper, we introduce this creation chain. We present our software engineering support for the chain, the component framework SemanticMM4U. The canonical processes supported by the creation chain and SemanticMM4U framework are described in detail. We also provide an explicit mapping of SemanticMM4U framework components to the processes and argue for the benefits of defining canonical processes for creating personalized semantically rich multimedia presentations. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Authoring of multimedia content can be considered as composing media assets such as images, videos, text, and audio in time, space, and interaction into a coherent multimedia presentation. Personalization of such content means that it reflects the users’ or user groups’ profile information and context information. Enriching the multimedia content with semantically rich metadata allows for a better search and retrieval of the content. To actually create personalized semantically-rich multimedia content, a manual authoring of the many different documents for all the different users’ and user groups’ needs is not feasible. Rather a (semi-)automatic authoring of the content seems reasonable. We have analyzed in detail today’s approaches and systems for authoring, personalizing, and semantically enriching multimedia presentations. Based on this analysis, we derived a general creation chain for the (semi-)automatic generation of such content. In this paper, we introduce this creation chain. We present our software engineering support for the chain, the component framework SemanticMM4U. The canonical processes supported by the creation chain and SemanticMM4U framework are described in detail. We also provide an explicit mapping of SemanticMM4U framework components to the processes and argue for the benefits of defining canonical processes for creating personalized semantically rich multimedia presentations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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