Semi-automatic annotation and retrieval of visual content using the topic map technology

Authors:
Atta Badii;Chattun Lallah;Oleksandr Kolomiyets;Meng Zhu;Michael Crouch
Affiliations:
Intelligent Media Systems and Services Research Laboratory, University of Reading, United Kingdom;Intelligent Media Systems and Services Research Laboratory, University of Reading, United Kingdom;Intelligent Media Systems and Services Research Laboratory, University of Reading, United Kingdom;Intelligent Media Systems and Services Research Laboratory, University of Reading, United Kingdom;Intelligent Media Systems and Services Research Laboratory, University of Reading, United Kingdom
Venue:
VIS'08 Proceedings of the 1st WSEAS international conference on Visualization, imaging and simulation
Year:
2008

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Abstract

There are still major challenges in the area of automatic indexing and retrieval of multimedia content data for very large multimedia content corpora. Current indexing and retrieval applications still use keywords to index multimedia content and those keywords usually do not provide any knowledge about the semantic content of the data. With the increasing amount of multimedia content, it is inefficient to continue with this approach. In this paper, we describe the project DREAM, which addresses such challenges by proposing a new framework for semi-automatic annotation and retrieval of multimedia based on the semantic content. The framework uses the Topic Map Technology, as a tool to model the knowledge automatically extracted from the multimedia content using an Automatic Labelling Engine. We describe how we acquire knowledge from the content and represent this knowledge using the support of NLP to automatically generate Topic Maps. The framework is described in the context of film post-production.