Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/1880
Title: | Intelligent fashion styling using genetic search and neural classification | Authors: | Vuruşkan, Arzu İnce, Türker Bulgun, Ender Guzelis, Cuneyt |
Keywords: | Particle swarm optimization Genetic algorithm Fashion design Female body shapes Neural networks Styling recommendation Expert-System Design |
Publisher: | Emerald Group Publishing Ltd | Abstract: | Purpose - The purpose of this paper is to develop an intelligent system for fashion style selection for non-standard female body shapes. Design/methodology/approach - With the goal of creating natural aesthetic relationship between the body shape and the shape of clothing, garments designed for the upper and lower body are combined to fit different female body shapes, which are classified as V, A, H and O-shapes. The proposed intelligent system combines genetic algorithm (GA) with a neural network classifier, which is trained using the particle swarm optimization (PSO). The former, called genetic search, is used to find the optimal design parameters corresponding to a best fit for the desired target, while the task of the latter, called neural classification, is to evaluate fitness (goodness) of each evolved new fashion style. Findings - The experimental results are fashion styling recommendations for the four female body shapes, drawn from 260 possible combinations, based on variations from 15 attributes. These results are considered to be a strong indication of the potential benefits of the application of intelligent systems to fashion styling. Originality/value - The proposed intelligent system combines the effective searching capabilities of two approaches. The first approach uses the GA for identifying best fits to the target shape of the body in the solution space. The second is the PSO for finding optimal (with respect to training mean-squared error) weight and threshold parameters of the neural classifier, which is able to evaluate the fitness of successively evolved fashion styles. | URI: | https://doi.org/10.1108/IJCST-02-2014-0022 https://hdl.handle.net/20.500.14365/1880 |
ISSN: | 0955-6222 1758-5953 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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