Modelling Image Semantic Descriptions from Web 2.0 Documents using a Hybrid Approach.

Zakaria, Lailatul Qadri, Hall, Wendy and Lewis, Paul (2009) Modelling Image Semantic Descriptions from Web 2.0 Documents using a Hybrid Approach. In, 11th International Conference on Information Integration and Web-based Applications & Services (iiWAS2009), Kuala Lumpur, Malaysia, 14 - 16 Dec 2009.


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With the increasing amount of multimedia content on the web added as user generated content in Web 2.0 websites, conventional multimedia information retrieval is presented with new challenges. It is no longer possible to rely only on meta-data based retrieval but to consider also content based techniques combined with the collective knowledge generated by users’ contributions and geo-referenced meta-data. Tagging is a modest way to annotate such documents and fails to capture a full semantic description of the document content. This report concerns ongoing research to investigate a means to identify, model and utilise semantic descriptions of the user-generated content in Web 2.0 documents using a hybrid approach. The approach consists of three main components, natural language processing, image analysis and a shared knowledge base. In this paper we describe the complete model but, as the image analysis component is in its early stages, the results focus on the natural language processing and the knowledge base. We show that the additional use of these components can improve retrieval and analysis performance over that based only on Web 2.0 tags.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: 14 Dec 2009 to 16 Dec 2009
Keywords: Image semantic description, natural language analysis, knowledge base, ontology, semantic web.
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Web & Internet Science
ePrint ID: 268495
Accepted Date and Publication Date:
14 December 2009Published
Date Deposited: 10 Feb 2010 18:37
Last Modified: 31 Mar 2016 14:16
Further Information:Google Scholar

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