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Detecting Bias on Aesthetic Image Datasets

Detecting Bias on Aesthetic Image Datasets

Adrian Carballal, Luz Castro, Rebeca Perez, João Correia
Copyright: © 2014 |Volume: 5 |Issue: 2 |Pages: 13
ISSN: 1947-3117|EISSN: 1947-3125|EISBN13: 9781466653245|DOI: 10.4018/ijcicg.2014070104
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MLA

Carballal, Adrian, et al. "Detecting Bias on Aesthetic Image Datasets." IJCICG vol.5, no.2 2014: pp.62-74. http://doi.org/10.4018/ijcicg.2014070104

APA

Carballal, A., Castro, L., Perez, R., & Correia, J. (2014). Detecting Bias on Aesthetic Image Datasets. International Journal of Creative Interfaces and Computer Graphics (IJCICG), 5(2), 62-74. http://doi.org/10.4018/ijcicg.2014070104

Chicago

Carballal, Adrian, et al. "Detecting Bias on Aesthetic Image Datasets," International Journal of Creative Interfaces and Computer Graphics (IJCICG) 5, no.2: 62-74. http://doi.org/10.4018/ijcicg.2014070104

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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.

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