Lai, P orcid.org/0000-0002-8095-5928 and Westland, S orcid.org/0000-0003-3480-4755 (2020) Machine learning for colour Palette extraction from fashion runway images. International Journal of Fashion Design, Technology and Education, 13 (3). pp. 334-340. ISSN 1754-3266
Abstract
An important aspect of colour forecasting is the process of generating colour palettes to represent collections at fashion shows. Humans have traditionally done this manually, and can do it well, but there are often too many images and it becomes an unmanageable task. In this paper, automatic machine-learning methods are developed to generate colour palettes for a fashion show based on the runway images. A set of ground-truth data to test the models was constructed based on asking each of 22 participants to select three colours to represent each of the 48 images from a particular fashion show. A close agreement was shown between these data and the colours automatically generated using a model that incorporated both supervised and unsupervised machine learning. The work could be extended to analyse millions of images from social media feeds to provide data-driven insights for colour forecasting.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Textile Institute and Informa UK Ltd 2020. This is an author produced version of an article published in International Journal of Fashion Design, Technology and Education. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | colour extraction; colour palette; runway images |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Sep 2020 13:14 |
Last Modified: | 18 Jul 2022 10:30 |
Status: | Published |
Publisher: | Taylor and Francis |
Identification Number: | 10.1080/17543266.2020.1799080 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165324 |