Color Counting for Fashion, Art, and Design
Mohammed Al-Rawi

TL;DR
This paper introduces a novel color counting method based on cumulative color histograms for fashion and design, outperforming existing GMM, K-Means, and deep learning approaches in estimating the number of colors.
Contribution
The work presents the first machine-based solution for accurate color counting, leveraging a new histogram-based approach that improves over traditional methods.
Findings
The proposed method outperforms GMM, K-Means, and deep learning in accuracy.
Traditional models struggle to accurately estimate the number of colors.
The method has potential applications in fashion, art, and interior design.
Abstract
Color modelling and extraction is an important topic in fashion, art, and design. Recommender systems, color-based retrieval, decorating, and fashion design can benefit from color extraction tools. Research has shown that modeling color so that it can be automatically analyzed and / or extracted is a difficult task. Unlike machines, color perception, although very subjective, is much simpler for humans. That being said, the first step in color modeling is to estimate the number of colors in the item / object. This is because color models can take advantage of the number of colors as the seed for better modelling, e.g., to make color extraction further deterministic. We aim in this work to develop and test models that can count the number of colors of clothing and other items. We propose a novel color counting method based on cumulative color histogram, which stands out among other…
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Taxonomy
TopicsColor Science and Applications · Color perception and design · Image Enhancement Techniques
MethodsTest
