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

Abstract

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

A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry

“…Machine learning has been implemented in the F&A industry for sales prediction [38], trend analysis, color prediction [39], demand forecasting [40], fabric defect detection [41], predicting fabric behavior using mechanical properties [42].…”

Color image knowledge model construction based on ontology

“…Based on the model, the brand image of color is calculated to extract its physicalized product driven by logical relationship. Then, combined with the previous research on the popular color trend predictions, 51 research on the chaotic characteristics of the complex color system can be carried out.…”

A Novel Approach of Integrating Natural Language Processing Techniques with Fuzzy TOPSIS for Product Evaluation

“…Fortunately, grey system theory (GST) is an effective research method [26]. Generally, grey theory provides a technique for transferring information from black (unknown) to white (known), it has the advantage of using uncertain or scattered information to handle complex tasks [27], and its features are suitable for limited sample research [28]. What is more, the GST method takes the grey system of "small sample, little information" as the research object [7], so it has superiority in dealing with fuzzy or grey data effectively in the KE.…”