IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Approach to Minimize Bias on Aesthetic Image Datasets

Approach to Minimize Bias on Aesthetic Image Datasets
View Sample PDF
Author(s): Adrian Carballal (University of A Coruña, Spain), Luz Castro (University of A Coruña, Spain), Nereida Rodríguez-Fernández (University of A Coruña, Spain), Iria Santos (University of A Coruña, Spain), Antonino Santos (University of A Coruña, Spain)and Juan Romero (University of A Coruña, Spain)
Copyright: 2019
Pages: 17
Source title: Interface Support for Creativity, Productivity, and Expression in Computer Graphics
Source Author(s)/Editor(s): Anna Ursyn (University of Northern Colorado, USA)
DOI: 10.4018/978-1-5225-7371-5.ch010

Purchase

View Approach to Minimize Bias on Aesthetic Image Datasets on the publisher's website for pricing and purchasing information.

Abstract

Over the last few years, numerous studies have been conducted that have sought to address automatic image classification. These approaches have used a variety of experimental sets of images from several photography sites. In this chapter, the authors look at some of the most widely used in the field of computational aesthetics as well as the capacity for generalization that each of them offers. Furthermore, a set of images built up by psychologists is described in order to predict perceptual complexity as assessed by a closed group of persons in a controlled experimental setup. Lastly, a new hybrid method is proposed for the construction of a set of images or a dataset for the assessment and classification of aesthetic criteria. This method brings together the advantages of datasets based on photography websites and those of a dataset where assessment is made under controlled experimental conditions.

Related Content

Annabel Jane Dover, Alex James Pearl. © 2023. 21 pages.
Gail Flockhart. © 2023. 37 pages.
Sally Waterman. © 2023. 23 pages.
Judith Martinez Estrada. © 2023. 26 pages.
Mireia Ludevid Llop. © 2023. 25 pages.
Richard T. Sawdon Smith. © 2023. 30 pages.
Panayotis Papadimitropoulos. © 2023. 21 pages.
Body Bottom