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A Semi-local Method for Image Retrieval

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

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Abstract

The visual content of an image is expressed by global or local features. Global features describe some properties of the image such as color, texture and shape. Local features were successfully used for object category recognition and classification to extract the local information from a set of interest points or regions. In this paper, we propose a semi-local method to extract the features based on the previous features extraction methods. Our technique is called the “Spatial Pyramid Matching: SPM”. It works by partitioning the image into increasingly fine sub-regions (or blocs) and computing histograms of global features found inside each bloc.

The results obtained by the proposed method are illustrated through some experiments on Wang and Holidays Dataset. The obtained Results show the simplicity and efficiency of our proposal.

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Notes

  1. 1.

    https://lear.inrialpes.fr/~jegou/data.php.

  2. 2.

    http://wang.ist.psu.edu/docs/related/.

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Author information

Authors and Affiliations

  1. MIRACL-ISIMS, BP242, City Ons, 3021, Sfax, Tunisia

    Hanen Karamti

  2. Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia

    Hanen Karamti

Authors
  1. Hanen Karamti
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Correspondence to Hanen Karamti .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Karamti, H. (2020). A Semi-local Method for Image Retrieval. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_16

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