Many different fashion related computer vision applications have been developed over the past few years. However, as an important attribute of fashion garments, color in fashion is rarely studied subject, and a color name matching algorithm is highly desired by the online fashion
community that maps a garment color from an image to a verbal description. As a continuation of our previous nearest-neighbor-based fashion color matching method, in this paper, we propose a psychophysical experiment to collect fashion color naming data and a new data-driven classification
model using random forest for color name classification. Our reversed color naming experiment uses a simple and straightforward procedure to extract users’ color naming schema. The random-forest classifier utilizes a set of linear and non-linear features in CIELab color space. It achieves
more than 80% accuracy, and shows great improvement over our nearest-neighbor-based model. Furthermore, this data-driven approach also has the ability of actively and dynamically learn and improve the algorithm; and it is also able to learn new users’ color vocabularies.
No References for this article.
No Supplementary Data.
No Article Media
Document Type: Research Article
Publication date: January 13, 2019
More about this publication?
For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.
Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.