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Fuzzy Clustering to Encode Contextual Information in Artistic Image Classification

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1602))

Abstract

Automatic art analysis comprises of utilizing diverse processing methods to classify and categorize works of art. When working with this kind of pictures, we have to take under consideration different considerations compared to classical picture handling, since works of art alter definitely depending on the creator, the scene delineated or their aesthetic fashion. This extra data improves the visual signals gotten from the images and can lead to better performance. However, this information needs to be modeled and embed alongside the visual features of the image. This is often performed utilizing deep learning models, but they are expensive to train. In this paper we utilize the Fuzzy C-Means algorithm to create a embedding strategy based on fuzzy memberships to extract relevant information from the clusters present in the contextual information. We extend an existing state-of-the-art art classification system utilizing this strategy to get a new version that presents similar results without training additional deep learning models.

Javier Fumanal Idocin and Humberto Bustince’s research has been supported by the project PID2019-108392GB I00 (AEI/10.13039/501100011033). Zdenko Takáč and L’ubomíra Horanská’s research has been supported by the grant VEGA 1/0267/21.

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Author information

Authors and Affiliations

  1. Public University of Navarra, Pamplona, Spain

    Javier Fumanal-Idocin & Humberto Bustince

  2. Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia

    Zdenko Takáč & L’ubomíra Horanská

  3. DaSCI Research Institute and DECSAI, University of Granada, Granada, Spain

    Oscar Cordon

Authors
  1. Javier Fumanal-Idocin
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  2. Zdenko Takáč
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  3. L’ubomíra Horanská
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  4. Humberto Bustince
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  5. Oscar Cordon
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Corresponding author

Correspondence to Javier Fumanal-Idocin .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

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Fumanal-Idocin, J., Takáč, Z., Horanská, L., Bustince, H., Cordon, O. (2022). Fuzzy Clustering to Encode Contextual Information in Artistic Image Classification. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-08974-9_28

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