Construction of the PSO-LSSVM prediction model for sleeve pattern dimensions based on garment flat recognition

Abstract

PurposeThe main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.Design/methodology/approachFor this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.FindingsThe experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.Originality/valueBy constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.

A Fine-grained Attributes Recognition Model for Clothing Based on Improved the CSPDarknet and PAFPN Network

“…This algorithm performs well in identifying clothing attributes such as shirt collar shape. Li et al [19] automatically identi ed and segmented the sleeves of shirts and optimized the LSSVM parameters by using PSO algorithm, which achieved better results in the case of small samples. Zhu et al [20] proposed sRA-Net to accurately obtain attributes representations by utilizing multiple latent relationships in clothing images to improve the performance of fashion attributes recognition.…”

Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions

“…Compared with the results of Liu et al [6], who used a BPNNbased model, the mean square error (MSE) and standard error (SE) were 2.06 ± 0.2. Using the PSO-LSSVM model, Li et al [14] could only predict the sleeve sizes at MSE and SE 7 displays the error between the estimated and ground-truth values. Among these results, the extreme of the maximum error in the testing set was 0.3720 cm, and that of the average error was 0.1182 cm, aligning with the GB/T 23698-2009 standard [52].…”

“…Compared with the results of Liu et al [6], who used a BPNN-based model, the mean square error (MSE) and standard error (SE) were 2.06 ± 0.2. Using the PSO-LSSVM model, Li et al [14] could only predict the sleeve sizes at MSE and SE measures of 1.057 ± 0.06. As shown in Table 7, our proposed hybrid model predicts eight difficultto-measure lower body sizes, where the total MSE and SE were 0.0054 ± 0.07.…”

“…In recent years, research on predicting human body dimensions and shapes using artificial intelligence technologies has been widely conducted, mainly including various prediction methods such as multiple linear regression (MLR) [3][4][5], back-propagation (BP) ANN [6][7][8][9], radial basis function (RBF) ANN [2,10,11], and support vector regression (SVR) methods [12][13][14]. Su et al [3] used an MLR model that incorporates easily measurable features, including the thickness/width parameters of cross-sections, with the objective of constructing additional lower-body characteristics.…”

“…While the results of this method were said to be insensitive to race and gender, in practical application, there may be other unaccounted factors that challenge the model's robustness. Li et al [14] introduced a data-driven model based on particle swarm optimization (PSO) to optimize the leastsquares support vector machine (LSSVM) algorithm. This model was applied to solve the problems of garment style recognition and size prediction for pattern making.…”