Semantic representation of multimedia content: Knowledge representation and semantic indexing

  • Authors:

  • Phivos Mylonas;Thanos Athanasiadis;Manolis Wallace;Yannis Avrithis;Stefanos Kollias

  • Affiliations:

  • School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Department of Computer Science, University of Indianapolis, Athens Campus, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2008

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Abstract

In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories. Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. The first part of this work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic indexing, while the second part will continue on the use of the extracted semantic information for personalized retrieval.