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Application of semistructured data model to the implementation of semantic content-based video retrieval system

Conference Paper


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Abstract


  • Semantic indexing of a video document is a process

    that performs the identification of elementary and complex

    semantic units in the indexed document in order to create a

    semantic index defined as a mapping of semantic units into the

    sequences of video frames. Semantic content-based video retrieval

    system is a software system that uses a semantic index built over

    a collection of video documents to retrieve the sequences of video

    frames that satisfy the given conditions.

    This work introduces a new multilevel view of data for the

    semantic content-based video retrieval systems. At the topmost

    level, we define an abstract view of data and we express it in a

    notation of enhanced conceptual modeling suitable for the formal

    representation of the semantic contents of video documents.

    A semistructured data model is proposed for the middle level

    representation of data. At the bottom level we implement a

    semistructured data model as an object-relational database. The

    completeness of the proposed approach is demonstrated through

    the mappings of a conceptual level into a semistructured level

    and into an object-relational organization of data. The paper

    describes a system of operations on semistructured data and

    shows how a sample query can be represented as an expression

    built from the operations.

Publication Date


  • 2007

Citation


  • Al-Safadi, L. A. E. & Getta, J. R. (2007). Application of semistructured data model to the implementation of semantic content-based video retrieval system. International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMN 2007 (pp. 217-222). USA: IEEE.

Scopus Eid


  • 2-s2.0-47849104945

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9300&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1965

Start Page


  • 217

End Page


  • 222

Abstract


  • Semantic indexing of a video document is a process

    that performs the identification of elementary and complex

    semantic units in the indexed document in order to create a

    semantic index defined as a mapping of semantic units into the

    sequences of video frames. Semantic content-based video retrieval

    system is a software system that uses a semantic index built over

    a collection of video documents to retrieve the sequences of video

    frames that satisfy the given conditions.

    This work introduces a new multilevel view of data for the

    semantic content-based video retrieval systems. At the topmost

    level, we define an abstract view of data and we express it in a

    notation of enhanced conceptual modeling suitable for the formal

    representation of the semantic contents of video documents.

    A semistructured data model is proposed for the middle level

    representation of data. At the bottom level we implement a

    semistructured data model as an object-relational database. The

    completeness of the proposed approach is demonstrated through

    the mappings of a conceptual level into a semistructured level

    and into an object-relational organization of data. The paper

    describes a system of operations on semistructured data and

    shows how a sample query can be represented as an expression

    built from the operations.

Publication Date


  • 2007

Citation


  • Al-Safadi, L. A. E. & Getta, J. R. (2007). Application of semistructured data model to the implementation of semantic content-based video retrieval system. International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMN 2007 (pp. 217-222). USA: IEEE.

Scopus Eid


  • 2-s2.0-47849104945

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9300&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1965

Start Page


  • 217

End Page


  • 222