Combining visual semantics and texture characterizations for precision-oriented automatic image retrieval

  • Authors:
  • Mohammed Belkhatir

  • Affiliations:
  • Laboratoire CLIPS-IMAG, Université Joseph Fourier, Grenoble, France

  • Venue:
  • ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
  • Year:
  • 2005

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Abstract

The growing need for ‘intelligent' image retrieval systems leads to new architectures combining visual semantics and signal features that rely on highly expressive frameworks while providing fully-automated indexing and retrieval processes. Indeed, addressing the issue of integrating the two main approaches in the image indexing and retrieval literature (i.e. signal and semantic) is a viable solution for achieving significant retrieval quality. This paper presents a multi-facetted framework featuring visual semantics and signal texture descriptions for automatic image retrieval. It relies on an expressive representation formalism handling high-level image descriptions and a full-text query framework in an attempt to operate image indexing and retrieval operations beyond trivial low-level processes and loosely-coupled state-of-the-art systems. At the experimental level, we evaluate the retrieval performance of our system through recall and precision indicators on a test collection of 2500 photographs used in several world-class publications.