Color Trend Analysis using Machine Learning with Fashion Collection Images

IF 2.4 4区 管理学 Q3 BUSINESS Clothing and Textiles Research Journal Pub Date : 2021-03-03 DOI:10.1177/0887302X21995948
Ahyoung Han, Jihoon Kim, Jaehong Ahn
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引用次数: 6

Abstract

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.
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使用机器学习对时装系列图像进行色彩趋势分析
时尚色彩趋势是直接影响品牌销售的重要营销要素。潘通等组织通过每年预测调色板,在专业色彩标准方面拥有全球权威。然而,问题仍然是,时装设计师是否会在时装秀上使用这些颜色来引导季节性时尚趋势。本研究通过机器学习分析时装系列的图像数据,通过网络抓取t台图像,通过机器学习分离身体和服装元素,使用k-means算法定义颜色芯片的选择,并分析Pantone调色板(16种颜色)与分析颜色芯片之间的相似性,从而获得可测量的结果。Pantone趋势和时装系列中使用的颜色之间的差距进行了定量分析,发现是显著的。这项研究表明了机器学习在时尚行业指导生产的潜力,并建议进一步研究扩展到其他设计变量。
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来源期刊
CiteScore
5.30
自引率
5.30%
发文量
12
期刊介绍: Published quarterly, Clothing & Textiles Research Journal strives to strengthen the research base in clothing and textiles, facilitate scholarly interchange, demonstrate the interdisciplinary nature of the field, and inspire further research. CTRJ publishes articles in the following areas: •Textiles, fiber, and polymer science •Aesthetics and design •Consumer Theories and Behavior •Social and psychological aspects of dress or educational issues •Historic and cultural aspects of dress •International/retailing/merchandising management and industry analysis
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