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Combining Visual Semantics and Texture Characterizations for Precision-Oriented Automatic Image Retrieval

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Advances in Information Retrieval (ECIR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3408))

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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.

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Author information

Authors and Affiliations

  1. Laboratoire CLIPS-IMAG, Université Joseph Fourier, Grenoble, France

    Mohammed Belkhatir

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  1. Mohammed Belkhatir
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Editors and Affiliations

  1. Departamento de Electrónica y Computación, Universidad de Santiago de Compostela, Spain

    David E. Losada

  2. Departamento de Ciencias de la Computación e Inteligencia Artificial E.T.S.I. Informática y de Telecomunicación, Universidad de Granada, 18071, Granada, Spain

    Juan M. Fernández-Luna

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Belkhatir, M. (2005). Combining Visual Semantics and Texture Characterizations for Precision-Oriented Automatic Image Retrieval. In: Losada, D.E., Fernández-Luna, J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_33

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  • DOI: https://doi.org/10.1007/978-3-540-31865-1_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25295-5

  • Online ISBN: 978-3-540-31865-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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