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Discovering Features Contexts from Images Using Random Indexing

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Combinatorial Image Analysis (IWCIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8466))

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Abstract

Random Indexing is a recent technique for dimensionality reduction while creating Word Space model from a given text. The present work explores the possible application of Random Indexing in discovering feature semantics from image data. The features appearing in the image database are plotted onto a multi-dimensional Feature Space using Random Indexing. The geometric distance between features is used as an indicative of their contextual similarity. K-means clustering is used to aggregate similar features. In this paper, we show that the Feature Space model based on Random Indexing can be used effectively to constellate similar features. The proposed clustering approach has been applied to the Corel databases and motivating results have obtained.

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Author information

Authors and Affiliations

  1. Institut Supérieur de Gestion, LARODEC Laboratory, University of Tunis, Tunisia

    Haïfa Nakouri & Mohamed Limam

  2. Dhofar University, Oman

    Mohamed Limam

Authors
  1. Haïfa Nakouri
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  2. Mohamed Limam
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Editor information

Editors and Affiliations

  1. Department of Computer and Information Sciences, State University of New York at Fredonia, 280 Central Ave, 14063, Fredonia, NY, USA

    Reneta P. Barneva

  2. Mathematics Department, SUNY Buffalo State College, 14222, Buffalo, NY, USA

    Valentin E. Brimkov

  3. Department of Mathematics, Brno University of Technology, 616 69, Brno, Czech Republic

    Josef Šlapal

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© 2014 Springer International Publishing Switzerland

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Nakouri, H., Limam, M. (2014). Discovering Features Contexts from Images Using Random Indexing. In: Barneva, R.P., Brimkov, V.E., Šlapal, J. (eds) Combinatorial Image Analysis. IWCIA 2014. Lecture Notes in Computer Science, vol 8466. Springer, Cham. https://doi.org/10.1007/978-3-319-07148-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-07148-0_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07147-3

  • Online ISBN: 978-3-319-07148-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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