Integrating Image Segmentation and Classification for Fuzzy Knowledge-Based Multimedia Indexing

  • Authors:

  • Thanos Athanasiadis;Nikolaos Simou;Georgios Papadopoulos;Rachid Benmokhtar;Krishna Chandramouli;Vassilis Tzouvaras;Vasileios Mezaris;Marios Phiniketos;Yannis Avrithis;Yiannis Kompatsiaris;Benoit Huet;Ebroul Izquierdo

  • Affiliations:

  • Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Greece;Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas (CERTH), Greece;Département Multimédia, Institut Eurécom, France;Department of Electronic Engineering, Queen Mary University of London, UK;Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas (CERTH), Greece;Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Greece;Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Greece;Informatics and Telematics Institute, Centre for Research and Technology Hellas (CERTH), Greece;Département Multimédia, Institut Eurécom, France;Department of Electronic Engineering, Queen Mary University of London, UK

  • Venue:
  • MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
  • Year:
  • 2009

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Abstract

In this paper we propose a methodology for semantic indexing of images, based on techniques of image segmentation, classification and fuzzy reasoning. The proposed knowledge-assisted analysis architecture integrates algorithms applied on three overlapping levels of semantic information: i) no semantics, i.e. segmentation based on low-level features such as color and shape, ii) mid-level semantics, such as concurrent image segmentation and object detection, region-based classification and, iii) rich semantics, i.e. fuzzy reasoning for extraction of implicit knowledge. In that way, we extract semantic description of raw multimedia content and use it for indexing and retrieval purposes, backed up by a fuzzy knowledge repository. We conducted several experiments to evaluate each technique, as well as the whole methodology in overall and, results show the potential of our approach.