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Intelligent Annotation of Digital Content

Annotation of digital content is both a pre-requisite and an objective in machine learning and understanding. Making adaptive machines to recognize objects and events in signals, images and videos, or even meanings in text documents, requires amounts of training data usually annotated by humans. The output of such machines can also be regarded as a set of annotations indicating one or more categories in which an object or an event may belong to, or as a set of semantic identifiers. The importance of data annotation extends well beyond the supervised paradigm; as for example the evaluation of unsupervised pattern recognition and data mining algorithms, and the simplified access to data. The research field of data annotation is undoubtedly broad with a long history across a variety of domains. However the open issues are still plenty especially regarding the generality and the applicability of methods on real-world data. This special session focuses on great challenges of the contemporary world where automatic and semi-automatic annotation can play a significant role with impact on the advancement of artificial intelligence. The session will accept only novel, high quality contributions on either theoretical issues or applications on relevant domains that are of high interest for the participants of the conference. All authors are encouraged to use publicly available data for experimental validation, enabling the reproducibility of the results presented in the papers.

Organizer: Dimitris K. Iakovidis, Technological Educational Institute of Lamia (Co-funded by EU Project DEBUGIT).

 Topics:

  • Automatic or semi-automatic annotation of free texts, signals, images and videos
  • Multimedia content understanding
  • Incremental / active learning for annotation
  • Scalable annotation
  • Formalization of annotation
  • Semantically-aware / ontology-based annotation
  • Uncertainty-aware annotation of real-world data
  • Semantically-aware unsupervised annotation methods
  • Generic tools for efficient and effective annotation of digital content
  • Applications of semantic web technologies
  • Biomedical and other applications of intelligent annotation methods

Program Committee:

  • Daniel Schöber, University of Freiburg, Germany
  • Michalis Zervakis, Technical University of Crete, Greece
  • Michalis Savelonas, University of Houston, USA
  • Nikos Papamarkos, University of Thrace, Greece
  • Stavros Karkanis, Technological Educational Institute of Lamia, Greece
  • Vassileios Mezaris, Informatics and Telematics Institute, CERTH, Greece