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Objective evaluation of optical illusion skirt based on image texture features

Wenda Wei (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China) (Zhejiang Province Engineering Laboratory of Clothing Digital Technology, Hangzhou, China) (China Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism, Hangzhou, China)
Chengxia Liu (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China) (Zhejiang Province Engineering Laboratory of Clothing Digital Technology, Hangzhou, China) (China Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism, Hangzhou, China)
Jianing Wang (School of Fashion Design and Engineering, Zhejiang Sci-Tech University, Hangzhou, China) (Zhejiang Province Engineering Laboratory of Clothing Digital Technology, Hangzhou, China) (China Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism, Hangzhou, China)

International Journal of Clothing Science and Technology

ISSN: 0955-6222

Article publication date: 28 October 2021

Issue publication date: 23 November 2021

131

Abstract

Purpose

Nowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.

Design/methodology/approach

Firstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.

Findings

Results show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.

Originality/value

Compared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.

Keywords

Acknowledgements

This work was supported by the Natural Science Foundation of Zhejiang Province under Grant No. LY20E050017, the Key Laboratory of Inheritance of Silk Culture and Digitalization Technology of Product Design and by the Ministry of Culture and Tourism.

Citation

Wei, W., Liu, C. and Wang, J. (2021), "Objective evaluation of optical illusion skirt based on image texture features", International Journal of Clothing Science and Technology, Vol. 33 No. 5, pp. 859-875. https://doi.org/10.1108/IJCST-10-2020-0163

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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