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Detecting Bias on Aesthetic Image Datasets

Author

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  • Adrian Carballal

    (Department of Information and Communication Technologies, University of A Coruña, A Coruña, Spain)

  • Luz Castro

    (Department of Information and Communication Technologies, University of A Coruña, A Coruña, Spain)

  • Rebeca Perez

    (Department of Information and Communication Technologies, University of A Coruña, A Coruña, Spain)

  • João Correia

    (Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal)

Abstract

In recent years, there have been attempts to discover the principles that determine the value of aesthetics in the domain of computing. Many and diverse studies have tried in some way to capture these principles through technical characteristics. To this end, helped by the ease of Internet data acquisition, datasets of images have been published which were obtained online at random from websites and photography competitions. To guarantee the validity of a system of aesthetic image classification, one must first guarantee its capacity for generalization. This paper studies how the indiscriminate selection of images can affect the generalization capacity obtained by a binary classifier.

Suggested Citation

  • Adrian Carballal & Luz Castro & Rebeca Perez & João Correia, 2014. "Detecting Bias on Aesthetic Image Datasets," International Journal of Creative Interfaces and Computer Graphics (IJCICG), IGI Global, vol. 5(2), pages 62-74, July.
    • Handle: RePEc:igg:jcicg0:v:5:y:2014:i:2:p:62-74
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    Cited by:

    1. Adrian Carballal & Carlos Fernandez-Lozano & Nereida Rodriguez-Fernandez & Luz Castro & Antonino Santos, 2019. "Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach," Complexity, Hindawi, vol. 2019, pages 1-12, January.

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