A novel method for fabric color transfer

During the color design process, it is critical to create diversified color schemes for various consumer groups. It is also important to allow members in a design team to brainstorm for creative ideas. A theory of inspecting natural color elements was proposed in this study based on the CIE 1976 (L*,a*,b*) (CIE 1976) color space and the fuzzy c‐means clustering method. A calculation model was built to determine people's preference for colors based on a serialized system. To determine which factors affect the prediction of consumers' preference for a series of products, a gray relational color preference system was used in combination with gray relational grade so that a designer can obtain reasonable prediction results. The proposed system recommends not only the optimal colors for a product but also the optimal color scheme for a series of products.

Section: Study Process and Methodsmentioning

confidence: 99%

A systematic approach of predicting color preference on the basis of gray relational grade

Lin

2019

“…Cluster analysis used in image segmentation is to group each pixel based on colour similarity among pixels. Fuzzy C‐Means (FCM) algorithm is a commonly used cluster analysis, and it can obtain optimal clustering in image colour separation . Applying FCM to image segmentation is increasingly widespread.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fuzzy C-Means (FCM) algorithm is a commonly used cluster analysis, and it can obtain optimal clustering in image colour separation. 5,19 Applying FCM to image segmentation is increasingly widespread. Effective colour clustering and substituting the data in prediction models is the main focus of this study, and hence how to retrieve optimal clustering result is of importance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Designers search for design inspiration through cameras, fashion magazines, works of art, and colour samples. Regardless of design in trend or clothing, different emotional responses towards various colours will be considered as a basic element . Thus, the competitive commercial environment urges customers to pay attention towards fashion in products.…”

Section: Introductionmentioning

confidence: 99%

“…Regardless of design in trend or clothing, different emotional responses towards various colours will be considered as a basic element. 5 Thus, the competitive commercial environment urges customers to pay attention towards fashion in products. Zhang indicated that colour is the main factor that influences fashion design through visual stimulus and psychological response.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An intelligent system for fashion colour prediction based on fuzzy C‐means and gray theory

Hsiao

Lee

Chen

et al. 2016

For design and manufacturing industries, to be able to capture the fashion trend is an essential factor that leads to winning a sale. However, colour predicting process in many organizations is not visible to the public. In order to provide colour trend to industries in advance, a predicting method is proposed in this study. In the method, the fuzzy c‐means was used to separate the collected colour data, then the minimum mean‐square error was used to place the similar colour clusters within different time point together and the gray model was adopted for prediction. In order to verify the prognostication of the system, four data announced by Pantone from spring 2014 to fall, 2015 were taken as the predicted samples and the colour for spring 2016 was predicted to compare with that in Pantone spring, 2016. The results show that the system has a high accuracy for predicting colour. The residual modified model constructed with the colour samples rearranged with MMSE has the best‐predicted result that ranged from 83.3% to 99.4%. It indicates that the result obtained with the rearranged samples is higher than that without rearrangement. Besides, the accuracy of the gray predicted results with residual modification would be more precise than the one without residual modification. Moreover, the value of mean squared error is quite low, which was ranged from 0.000025 to 0.0277. Therefore, the current intelligent predicting system satisfies the criteria of capturing colour in trend for enterprises. Moreover, it enables industries to make decisions for selecting the colour trend. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 273–285, 2017

Recoloring textile fabric images based on improved fuzzy clustering

Zou

Shen

et al. 2016

This article proposes a new recoloring method for textile fabric images based on improved fuzzy local information c-means (FLICM) clustering. In the clustering algorithm, the fuzzy factor was modified so that it can produce reliable segmentation in areas with rich details. With the obtained cluster labels and pixel-wise memberships, the color of each pixel is modeled as the linear combination of the two most dominant colors. The recoloring process was then conducted by replacing the specified dominant color with user-provided target colors. Experimental results showed that the proposed method can produce natural and faithful color appearance on both printed and yarn-dyed fabric images, and outperforms the state-of-the-art.