Home Research on She nationality clothing recognition based on color feature fusion with PSO-SVM
Article Open Access

Research on She nationality clothing recognition based on color feature fusion with PSO-SVM

  • Xiaojun Ding , Tao Li , Jingyu Chen and Fengyuan Zou EMAIL logo
Published/Copyright: December 13, 2023
Download Article (PDF)
AUTEX Research Journal
From the journal AUTEX Research Journal Volume 24 Issue 1

Abstract

Although the color characteristics of She nationality clothing are slightly different, there are multiple similarities in shapes and textures. Therefore, it is difficult to effectively distinguish different branches of She nationality clothing. To address this problem, this article, taking into account color feature fusion, proposes a recognition method based on a hybrid algorithm of particle swarm optimization and support vector machine (PSO-SVM). First, the color histogram and color moment (CM) feature descriptors were extracted from the five branches of She nationality clothing, and the color feature distribution of each branch was obtained. Then, color feature fusion is performed through optimization and dimensionality reduction of principal components. Furthermore, PSO was introduced to independently optimize parameter combinations. Finally, the different branches of She nationality clothing were automatically recognized. The results demonstrated that the proposed method could effectively distinguish different branches of She nationality clothing. Compared with the recognition accuracy of approaches using single-color histogram and CM feature, the performance of our proposed method was increased by 5.25 and 6.44%, respectively. When the penalty parameter γ and kernel parameter δ 2 of SVM were 123.29 and 1.16, respectively, the recognition accuracy of the model was the highest, reaching 98.67%. The proposed method could be a reference for the subdivision recognition of She nationality clothing.

Keywords: She nationality clothing; color feature fusion; PSO-SVM; clothing recognition

1 Introduction

The She nationality is a minority in southeast China with a unique dress culture. Still, it retains many distinctive clothing characteristics, which are a symbol of the nationality and a basis of ethnic cognition [1]. She nationality clothing can be mainly divided into five branches: Jing Ning, Fu An, Luo Yuan, Xia Pu, and Fu Ding branches. Nevertheless, they are similar in shape, material, and other aspects. For example, they are all T-shaped and made of ramie, with the dominant colors (DCs) being blue and black. Therefore, they are difficult to be effectively distinguished, which directly affects the recognition accuracy.

Nowadays, automatic recognition of national clothing through image recognition technology has become the mainstream research direction of computer vision. Nawaz et al. [2] realized the interclass recognition of tops, bottoms, suits, and other garments using the convolutional neural network. Fu and Liu [3] characterized the clothing silhouette information with feature size, reference point, and silhouette curve, and established the calculation model of silhouette form value and form ratio for style recognition. However, the aforementioned methods mainly focus on clothing styles with significant differences in silhouette and have poor recognition accuracy for styles with great similarity and subtle category differences [4]. Therefore, related scholars proposed to convert the color image into a grayscale one to reduce the color interference. Then the local fine-grained features such as the scale invariant feature transform combined with a bag-of-words model were applied to recognize the She nationality clothing with similar silhouettes [5]. Nevertheless, a single feature can only describe the style information from one perspective, which has a small scope of application and a number of limitations. To solve the aforementioned problems, Surakarin and Chongstivatana [6] extended the applicability of feature descriptors by feature-level fusion of weighted speeded-up robust features and local binary pattern through the bag of features. Yao and Keand [7] performed generic intelligent scenery textures (GIST) and speeded-up robust features (SURF) fusion through weight proportion, demonstrating that multifeature fusion could effectively improve the accuracy of style recognition.

In terms of the selection of the feature descriptors of the traditional national clothing, Xing et al. [8] extracted the color features through mean-shift clustering algorithm, completing the color expression of traditional national clothing. Liu et al. [9] characterized the colors of fabric images based on color histogram feature descriptors and applied the three-level weight method to extract the DC system representing the image information. In terms of establishing a recognition system based on color features, Zhang et al. [10] fused the feature sets of the DCs and color moments (CMs) based on the underlying color features, reaching 87% recognition accuracy. This also showed the effectiveness of color features in characterizing image information, which provides a research idea for this article. However, due to the similarities in shapes and textures, it is impossible to characterize the differences among them, and it is difficult to distinguish different branches of She nationality clothing with shape and texture feature descriptors. Therefore, considering the different branches of She clothing that have various decorative details and are difficult to recognize, this article recognizes different branches of She clothing by fusing color features.

This article proposes a method for the recognition of different branches of She nationality clothing based on color feature fusion with particle swarm optimization and support vector machine (PSO-SVM). First, the non-uniform quantization of the pixels of the three color channels H, S, and V was carried out in the hue saturation value (HSV) color space. Second, the color histogram and CM features were extracted as feature descriptors. Third, principal component analysis (PCA) was applied to reduce the dimension of the extracted color features. Finally, the clothing recognition model for different branches of She nationality clothing based on PSO-SVM was constructed, which provides a new research idea for the recognition of similar clothing subdivisions in the field of computer vision.

2 Experiments

2.1 Experimental samples

The garments of the five branches of the She nationality – Jing Ning, Fu An, Luo Yuan, Xia Pu, and Fu Ding branches – which are from China She Nationality Museum and related monographs, were selected as the experimental samples. There are 74 samples with 370 authentic images for each branch, as shown in Figure 1. The experiments were implemented on a PC with Intel(R) Core(TM)i7-10700 CPU and AMD Radeon R5 430 GUP. The operation system was Windows 10 with Matlab R2022a. Nikon 5200 was used as the camera device.

Figure 1 Experiment samples: (a) Jing Ning, (b) Fu An, (c) Luo Yuan, (d) Xia Pu, and (e) Fu Ding.
Figure 1

Experiment samples: (a) Jing Ning, (b) Fu An, (c) Luo Yuan, (d) Xia Pu, and (e) Fu Ding.

2.2 Color feature fusion

2.2.1 Color space conversion

The color space directly affects the color feature extraction results. The HSV color space, consisting of three color channels – hue (H), saturation (S), and value (V), conforms to the visual perception of human eyes [11,12]. In this article, the experimental images were converted from RGB to HSV. Then, the color histogram and CM features were extracted to represent the characteristics of She nationality clothing of different branches. Specifically, the color histogram was applied to reflect the color composition distribution, while the CMs were used to characterize the color information distribution.

2.2.2 Histogram color feature extraction

To obtain the effective features, non-uniform quantization was performed on the H, S, and V channels of the images. This purpose was to improve the efficiency of the recognition accuracy. The non-uniform quantization levels were set to be 16 for the H channel, and 4 for both S and V channels [13]. The three-color channel quantities were transformed into one-dimensional color feature vectors to reduce the feature dimensionality, as shown in the following formula:

(1) L = H × Q S × Q V + S × Q V + V ,

where L represents the merged one-dimensional variable; both Q S and Q V are set to 4, representing the quantization series of the S and V channels, respectively. Formula (2) is obtained as follows:

(2) L = 16 H + 4 S + V .

As shown in formula (2), the quantized color space consisted of 256 color eigenvalues. The eigenvector { L 0 , L 1 , … . , L 255 } of the color histogram was obtained.

2.2.3 CM feature extraction

To compensate for the problem that the color histogram ignores the pixel location distribution, this article introduced CMs for effective supplementation. The color information was mainly distributed in the lower-order moments. Thus, the first-order moments ( μ i ), second-order moments ( σ i ), and third-order moments ( ω i ) of the image were extracted, as shown in formula (3). In the experiment, nine CM features of the She nationality clothing image were extracted to form a one-dimensional CM feature vector { μ H , μ S , μ V , σ H , σ S , σ V , ω H , ω S , ω V } [14]:

(3) μ i = 1 N ∑ j = 1 N P i , j , σ i = 1 N ∑ j = 1 N ( P i , j − μ i ) 2 1 / 2 ω i = 1 N ∑ j = 1 N ( P i , j − μ i ) 3 1 / 3 , ,

where N represents the total number of pixels and P i , j represents the ith color channel of the jth pixel.

2.2.4 Principal component optimization feature fusion

The large data dimension leads to excessive training time for clothing recognition models. It directly affects the recognition accuracy. Therefore, PCA was applied to reduce the feature dimension of the She nationality clothing feature vector. The optimized fused color feature was obtained after dimensionality reduction, as shown in formula (4).

The dimensionality reduction of the original data was achieved by calculating the variance contribution of each principal component and selecting m (m < p) principal components according to the cumulative variance contribution [15].

(4) Y 1 = α 11 X 1 + α 12 X 2 + ⋯ + α 1 p X p Y 2 = α 21 X 1 + α 22 X 2 + ⋯ + α 2 p X p ⋯ ⋯ ⋯ Y p = α p 1 X 1 + α p 2 X 2 + ⋯ + α p p X p ,

where: X 1 , X 2 , ⋯ , X p are the p indexes of sample X , parameter α is the index coefficient, and the comprehensive index Y i (i = 1,2,…,  p ) is a linear combination of p indexes.

2.3 Construction of the PSO-SVM recognition model

In this article, SVM was selected as a clothing recognition classifier. The kernel function was introduced to map samples to a high-dimensional linear space as the solution, which has the advantage of a short training period and is suitable for small samples. Considering the linear indivisibility of the experimental samples, the radial basis functions were used as the kernel function in SVM. As the combination of penalty parameter ( γ ) and kernel parameter ( δ 2 ) in the radial basis function has a large impact on the model prediction and generalization performance, and SVM cannot optimize the parameters in a large parameter space autonomously, PSO was introduced to realize the autonomous optimization of the combination [16].

PSO is a highly efficient and fast convergent swarm intelligence algorithm [17]. Each particle’s own state is described by a set of position and velocity vectors that respectively represent the feasible solution of the problem and its direction of motion in the search space. The particle continuously learns the group optimal solution and neighbor optimal solution it discovers, and finally achieves a global optimal search. Particle velocity and position are shown in formulas (5) and (6):

(5) v id k + 1 = w v id k + c 1 r 1 ( p id − u id k ) + c 2 r 2 ( p gd − u id k ) ,

(6) u id k + 1 = u id k + v id k + 1 ,

where c 1 and c 2 represent the learning factors, indicating local and global search capabilities, respectively, and r 1 and r 2 random functions in the [0,1] interval.

The overall construction process of the PSO-SVM model in this article is shown in Figure 2.

Figure 2 Flowchart of PSO-SVM model.
Figure 2

Flowchart of PSO-SVM model.

3 Results and discussion

3.1 Color features of the She nationality clothing

3.1.1 Analysis of the color features

To ensure the integrity of color feature extraction of She nationality clothing, the 256_d color histogram and the 9_d CM feature vectors were extracted in the HSV color space model. They were used to represent the information characteristics of different branches of She nationality clothing. Moreover, the color quantification method was used for the analysis (Table 1).

Table 1

Color distribution of She nationality clothing

As shown in Table 1, the color composition of different branches is relatively similar. The DCs are black and dark blue, whose brightness value is close to 0%, with relatively weak brightness contrast. The proportions of the DC for each branch range from 55.91 to 96.65%. Specifically, Luo Yuan’s DC proportion is significantly lower than other branches, and Jing Ning reaches the highest of 96.65%.

The matching colors (MCs) of clothing of different branches of the She nationality are mainly vermilion, ocher, etc., and small-area color designs such as embroidery and lace form a sharp contrast in brightness and saturation. Among the five branches, the proportions of the MCs range from 3.35 to 44.09%. It can be seen from the comparison of Luo Yuan > Fu Ding > Xia Pu > Fu An > Jing Ning that Luo Yuan branch is the richest in color.

Furthermore, Jing Ning is concentrated in medium–low saturation and medium brightness areas. Fu An is concentrated in medium–high saturation and medium brightness areas. Luo Yuan is concentrated in medium–low saturation and medium–high brightness areas. Xia Pu is concentrated in medium saturation and medium brightness areas, and Fu Ding is concentrated in high saturation and medium brightness areas.

3.1.2 Feature fusion for Initial parameter settings of PSO

In the feature level of the five branches of She nationality clothing, the 256_d color histogram and the 9_d CM feature vectors were fused to obtain 265 fused color feature vector sets. According to PCA, the cumulative contribution rate of the principal component was obtained, as shown in Figure 3. It could be seen that the cumulative contribution rate of the 60th principal component has reached 99.5%, which indicates that the first 60 principal components contain most of the information in the fused feature vector of She nationality clothing.

Figure 3 Accumulative contribution rate.
Figure 3

Accumulative contribution rate.

To analyze the influence of cumulative contribution on recognition results, 20 sets of data with different principal component dimensions were selected at the cumulative contribution rates ranging from 90 to 100%. The relationship between the cumulative contribution rate and recognition accuracy was analyzed. The recognition model was trained by the training set, and the recognition results were obtained by the testing set. The recognition accuracy is shown in the following formula:

(7) Accuracy = sum ( Y label = y label ) / Length ( y label ) × 100 % ,

where Y label represents the true category and y label represents the recognition category of the model.

Figure 4 shows that when the cumulative contribution rate is 91.4%, the recognition accuracy of the first 22 principal components is the highest, reaching 98.57%. Therefore, on the premise of ensuring the recognition accuracy, 60 feature vectors were further downscaled and optimized, and 22 fused feature matrices characterizing the She nationality clothing images were obtained.

Figure 4 Comparison of recognition effect.
Figure 4

Comparison of recognition effect.

3.2 She nationality clothing recognition based on color fusion features

In the recognition model, the dataset was divided into the training set and testing set by 300:70 random samples. Then, the fused color feature matrix obtained by feature extraction on the training set, and the category labels of different branches of She nationality clothing were respectively selected as input and output. The initial settings of the parameters of the PSO-SVM model are shown in Table 2. The PSO was applied to autonomously optimize SVM parameters and obtain the optimal fitness γ = 123.29, δ 2 = 1.16, and the weighted average recognition accuracy was 96.5%, as shown in Figure 5.

Table 2

Initial parameter settings of PSO-SVM

Parameter variable Parameter value Parameter variable Parameter value
Learning factor c 1 0.5 Particle rate [ V min , V max ] [−0.25, 0.25]
Learning factor c 2 0.5 Particle take value [ pop min , pop max ] [0.001, 200]
Population size 20 Inertia factor [ ω min , ω max ] [0.4, 0.6]
Evolution times 200
Figure 5 SVM parameter selection results.
Figure 5

SVM parameter selection results.

The PSO-SVM recognition model was constructed by PCA. The 22 color fusion features and the corresponding branch categories after dimensionality reduction were analyzed. Table 3 shows the recognition results. It can be seen that the PSO-SVM recognition model has a good recognition effect on the five branches of She nationality clothing. However, among the 15 pieces of clothing from the Fu Ding branch, one was misidentified as Xia Pu’s. The main reason for misidentification is that there are “plum blossoms” in the collar area of the latter branch. Compared with the whole garment, the color characteristics of the “plum blossoms” are relatively small and cannot form sufficient feature difference, making it difficult to distinguish or even misidentify.

Table 3

Recognition results of She nationality clothing in different branches

Branch Jing Ning Fu An Luo Yuan Xia Pu Fu Ding Accuracy (%)
Jing Ning 9 0 0 0 0 100
Fu An 0 11 0 0 0 100
Luo Yuan 0 0 14 0 0 100
Xia Pu 0 0 0 21 0 100
Fu Ding 0 0 0 1 14 93.33

3.3 Comparison of recognition methods of different branches of the She nationality clothing

3.3.1 Comparative analysis of color feature recognition methods

To search for suitable color feature descriptors for different branches of She nationality clothing, four clothing recognition methods, including two single-color feature methods, one multi-color feature fusion method, and one method proposed in this article, were established (Table 4). Figure 6 shows the training time and recognition accuracy of the recognition methods.

Table 4

Comparison of recognition methods

Color recognition method Method CM feature vector Color histogram feature vector PCA Optimal parameters
γ δ 2
Single-color feature I √ 200.43 1.52
Ⅱ √ 198.67 1.31
Multi-color feature fusion Ⅲ √ √ 156.84 1.23
Proposed method Ⅳ √ √ √ 123.29 1.16

“√” indicates that the method uses the corresponding construction module.

Figure 6 Comparison of training time and recognition accuracy.
Figure 6

Comparison of training time and recognition accuracy.

As shown in Figure 6, methods II and III spend more time in training, and method I takes the shortest time. It shows that the training time is positively correlated with the feature dimension. It can be seen from the recognition accuracy comparison that method IV > method III > method II > method I. PCA could retain the original important features of She nationality clothing of different branches after dimensionality reduction. Besides, the average recognition accuracy improved by 5.01%. Considering the training time and accuracy, method IV had the best recognition effect on different branches of She nationality clothing, with the recognition accuracy reaching 98.67%.

3.3.2 Comparative analysis of SVM parameter optimization methods

In response to the problem that SVM penalty parameter γ and the kernel parameter δ 2 are challenging to determine, various optimization-seeking algorithms, such as genetic algorithm (GA) [18] and PSO, have been used to solve the parameter-seeking problem. A comparison was performed to verify the effectiveness under the same test samples and search area. Figure 7 shows the results. The categories 1–5 represent Jing Ning, Fu An, Luo Yuan, Xia Pu, and Fu Ding, respectively.

Figure 7 Confusion matrix of She nationality clothing recognition: (a) PSO-SVM, (b) GA-SVM, and (c) SVM.
Figure 7

Confusion matrix of She nationality clothing recognition: (a) PSO-SVM, (b) GA-SVM, and (c) SVM.

As illustrated in Figure 7, relative to GA-SVM and SVM models, the PSO-SVM model’s recognition accuracy was improved by 5.07 and 9.67%, respectively. This is mainly because the GA algorithm is prone to fall into “premature convergence.” It has the characteristics of crossover and mutation and requires manual experience to set parameters. Compared with the GA algorithm, PSO-SVM can better recognize She nationality clothing.

4 Conclusions

In this article, the clothing recognition method based on PSO-SVM with color feature fusion was proposed. The main conclusions were drawn as follows:

  1. The 265 color histogram and CM feature vector sets of the five branches of She nationality were extracted, and the color feature distribution of each branch was obtained. The DCs of She nationality clothing are black and dark blue, taking a proportion ranging from 55.91 to 96.65%. Among the five branches, Jing Ning branch has the highest proportion in terms of DCs. The MCs are mainly vermilion, ocher, and other colors, with the proportion ranging from 3.35 to 44.09%. It can be seen from the comparison of Luo Yuan > Fu Ding > Xia Pu > Fu An > Jing Ning that Luo Yuan branch is the richest in color.

  2. The PSO-SVM recognition model for She nationality clothing with the fusion of color features was established. Compared with the models built by singer-color histogram and by CM features, the recognition accuracy of the proposed model was improved by 5.25 and 6.44%, respectively. When the penalty parameter γ and kernel parameter δ 2 were 123.29 and 1.16, respectively, the average recognition accuracy of the model was improved by 5.01, reaching 98.67%.

  3. Compared with the GA-SVM and SVM models, the prediction accuracy of the PSO-SVM recognition model improved by 5.06 and 9.67%, respectively.

  1. Funding information: This study was financially supported by the Key Laboratory of Ministry of Culture and Tourism Foundation of China (No. 19076223-B), Clothing Engineering Research Center of Zhejiang Province (No. 2019FZKF08), and Zhejiang Provincial Philosophy and Social Sciences Planning Project (No. 22NDJC077YB).

  2. Conflict of interest: Authors state no conflict of interest.

  3. Data availability statement: Data are available from the authors upon reasonable request.

References

[1] Chen, L. Y. (2012). Evolution causes analysis of She’s ancient costume from perspective of cultural change. Journal of Textile Research, 33(17), 111–115.Search in Google Scholar

[2] Nawaz, M. M. T., Hasan, R., Hasan, M. A., Hassan, M., Rahman, R. M. (2018). Automatic categorization of traditional clothing using convolutional neural network. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) (pp. 98–103). IEEE, Singapore.Search in Google Scholar

[3] Fu, B. L., Liu, X. G. (2019). An intelligent computational framework for the definition and identification of the womenswear silhouettes. International Journal of Clothing Science and Technology, 31(2), 158–180.10.1108/IJCST-08-2017-0128Search in Google Scholar

[4] Wu, H., Ding, X. J., Li, Q. M., Du, L., Zou, F. Y. (2019). Classification of women’s trousers silhouette using convolution neural network CaffeNet model. Journal of Textile Research, 40(4), 117–121.Search in Google Scholar

[5] Ding, X., Zou, C., Chen, J., Zou, F. (2016). Extraction and classification of She nationality clothing via visual features. Textile Research Journal, 86(12), 1259–1269.10.1177/0040517515609260Search in Google Scholar

[6] Surakarin, W., Chongstivatana, P. (2015). Classification of clothing with weighted SURF and local binary patterns. In 2015 International Computer Science and Engineering Conference (ICSEC) (pp. 1–6).10.1109/ICSEC.2015.7401409Search in Google Scholar

[7] Yao, L., Keand, H. (2018). Robust image retrieval for lacy and embroidered fabric. Textile Research Journal, 89(13), 2616–2625.10.1177/0040517518798648Search in Google Scholar

[8] Xing, L., Zhang, J., Liang, H. E., Li, Z. J., Liu, J. J. (2017). Intelligent inspection of dominant colors for Chinese traditional folk Yunjian. Journal of Textile Research, 38(11), 110–115, 123.Search in Google Scholar

[9] Liu, S., Jiang, Y., Luo, H. (2018). Attention-aware color theme extraction for fabric images. Textile Research Journal, 88(5), 552–565.10.1177/0040517516685278Search in Google Scholar

[10] Zhang, N., Xiang, J., Wang, L., Xiong, N., Gao, W., Pan, R. (2020). Image retrieval of wool fabric. Part II: based on low-level color features. Textile Research Journal, 90(7–8), 797–808.10.1177/0040517519881819Search in Google Scholar

[11] CinKo, O. U., Becerir, B. (2019). Dependence of colour difference formulae on regular changes of colour coordinates in CIELAB colour space. Industria Textila, 70(3), 248–254.10.35530/IT.070.03.1525Search in Google Scholar

[12] Sinsh, S., Batra, S. (2020). An efficient bi-layer content based image retrieval system. Multimedia Tools and Applications, 79(25), 17731–17759.10.1007/s11042-019-08401-7Search in Google Scholar

[13] Zhang, C. (2017). Research of object tracking algorithm based on color space nonuniform quantization and multi-information fusion (pp. 26–28). Changsha University of Science and Technology.Search in Google Scholar

[14] Jing, J., Li, Q., Li, P., Zhang, L. (2016). A new method of printed fabric image retrieval based on color moments and gist feature description. Textile Research Journal, 86(11), 1137–1150.10.1177/0040517515606378Search in Google Scholar

[15] Indrie, L., Bellemare, J., Zlatev, Z., Tripa, S., Diaz-Garcia, P., Montava, I., et al. (2021). Contemporary customized clothes using folk motifs. Industria Textila, 72(6), 632–638.10.35530/IT.072.06.1834Search in Google Scholar

[16] Yu, C. B., Xi, Z. W., Lu, Y. L., Tao, K. X., Yi, Z. (2020). K/S value prediction of cotton fabric using PSO-LSSVM. Textile Research Journal, 90(23–24), 2581–2591.10.1177/0040517520924750Search in Google Scholar

[17] Qiao, Y., Zhang, S., Wu, N., Wang, X., Li, Z., Zhou, M., et al. (2019). Data-driven approach to optimal control of ACC systems and layout design in large rooms with thermal comfort consideration by using PSO. Journal of Cleaner Production, 236, 11758.10.1016/j.jclepro.2019.07.053Search in Google Scholar

[18] Cheng, P., Chen, D., Wang, J. (2020). Research on underwear pressure prediction based on improved GA-BP algorithm. International Journal of Clothing Science and Technology, 33(4), 619–642.10.1108/IJCST-05-2020-0078Search in Google Scholar
Received: 2023-04-03
Accepted: 2023-11-08
Published Online: 2023-12-13

© 2024 by the authors, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  2. Characterization of viscoelastic properties of yarn materials: Dynamic mechanical analysis in the transversal direction
  3. Analysis of omni-channel implementations that are preferred by consumers in clothing sector
  4. Structural modeling and analysis of three-dimensional cross-linked braided preforms
  5. An experimental study of mechanical properties and comfortability of knitted imitation woven shirt fabrics
  6. Technology integration to promote circular economy transformation of the garment industry: a systematic literature review
  7. Research on T-shirt-style design based on Kansei image using back-propagation neural networks
  8. Research on She nationality clothing recognition based on color feature fusion with PSO-SVM
  9. Accuracy prediction of wearable flexible smart gloves
  10. Preparation and performance of stainless steel fiber/Lyocell fiber-blended weft-knitted fabric
  11. Development of an emotional response model for hospital gown design using structural equation modeling
  12. Preparation and properties of stainless steel filament/pure cotton woven fabric
  13. Facemask comfort enhancement with graphene oxide from recovered carbon waste tyres
  14. Use of enzymatic processes in the tanning of leather materials
  15. Optical-related properties and characterization of some textile fibers using near-infrared spectroscopy
  16. Network modeling of aesthetic effect for Chinese Yue Opera costume simulation images
  17. Predicting consumers’ garment fit satisfactions by using machine learning
  18. Non-destructive identification of wool and cashmere fibers based on improved LDA using NIR spectroscopy
  19. Study on the relationship between structure and moisturizing performance of seamless knitted fabrics of protein fibers for autumn and winter
  20. Antibacterial and yellowing performances of sports underwear fabric with polyamide/silver ion polyurethane filaments
  21. Numerical and experimental analysis of ballistic performance in hybrid soft armours composed of para-aramid triaxial and biaxial woven fabrics
  22. Phonetic smart clothing design based on gender awareness education for preschoolers
  23. Determination of anthropometric measurements and their application in the development of clothing sizing systems for women in the regions of the Republic of Croatia
  24. Research on optimal design of pleated cheongsam based on Kano–HOQ–Pugh model
  25. Numerical investigation of weaving machine heald shaft new design using composite material to improve its performance
  26. Corrigendum to “Use of enzymatic processes in the tanning of leather materials”
  27. Shaping of thermal protective properties of basalt fabric-based composites by direct surface modification using magnetron sputtering technique
  28. Numerical modeling of the heat flow component of the composite developed on the basis of basalt fabric
  29. Weft insertion guideway design based on high-temperature superconducting levitation
  30. Ultrasonic-assisted alkali hydrolysis of polyethylene terephthalate fabric and its effect on the microstructure and dyeing properties of fibers
  31. Comparative study on physical properties of bio-based PA56 fibers and wearability of their fabrics
  32. Investigation of the bias tape roll change time efficiency in garment factories
  33. Analysis of foot 3D scans of boys from Polish population
  34. Optimization of garment sewing operation standard minute value prediction using an IPSO-BP neural network
  35. Influence of repeated switching of current through contacts made of electroconductive fabrics on their resistance
  36. Numerical calculation of air permeability of warp-knitted jacquard spacer shoe-upper materials based on CFD
  37. Compact Spinning with Different Fibre Types: An Experimental Investigation on Yarn Properties in the Condensing Zone with 3D-Printed Guiding Device
  38. Modeling of virtual clothing and its contact with the human body
  39. Advances in personalized modelling and virtual display of ethnic clothing for intelligent customization
  40. Investigation of weave influence on flame retardancy of jute fabrics
  41. Balloonless spinning spindle head shape optimisation
  42. Research on 3D simulation design and dynamic virtual display of clothing flexible body
  43. Turkish textile and clothing SMEs: Importance of organizational learning, digitalization, and internationalization
  44. Corrigendum To: “Washing characterization of compression socks”
  45. Study on the promotion multiple of blood flow velocity on human epidermal microcirculation of volcanic rock polymer fiber seamless knitted fabric
  46. Bending properties and numerical analysis of nonorthogonal woven composites
  47. Bringing the queen mother of the west to life: Digital reconstruction and analysis of Taoist Celestial Beings Worshiping mural’s apparel
  48. Modeling process for full forming sports underwear
  49. Retraction of: Ionic crosslinking of cotton
  50. An observational study of female body shape characteristics in multiracial Malaysia
  51. Study on theoretical model and actual deformation of weft-knitted transfer loop based on particle constraint
  52. Design and 3D simulation of weft-knitted jacquard plush fabrics
  53. An overview of technological challenges in implementing the digital product passport in the textile and clothing industry
  54. Understanding and addressing the water footprint in the textile sector: A review
  55. Determinants of location changes in the clothing industry in Poland
  56. Influence of cam profile errors in a modulator on the dynamic response of the heald frame
  57. Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy
  58. 3D simulation of double-needle bar warp-knitted clustered pile fabrics on DFS
  59. Finite element analysis of heat transfer behavior in glass fiber/metal composite materials under constant heat load
  60. Price estimation and visual evaluation of actual white fabrics used for dress shirts and their photographic images
  61. Effect of gluing garment materials with adhesive inserts on their multidirectional drape and bending rigidity
  62. Optimization analysis of carrier-track collision in braiding process
  63. Numerical and experimental analysis of the ballistic performance of soft bulletproof vests for women
  64. The antimicrobial potential of plant-based natural dyes for textile dyeing: A systematic review using prisma
  65. Influence of sewing parameters on the skin–fabric friction
  66. Validation by experimental study the relationship between fabric tensile strength and weave structures
  67. Optimization of fabric’s tensile strength and bagging deformation using surface response and finite element in stenter machine
  68. Analysis of lean manufacturing waste in the process flow of ready-to-wear garment production in Nigeria
  69. An optimization study on the sol–gel process to obtain multifunctional denim fabrics
  70. Drape test of fully formed knitted flared skirts based on 3D-printed human body posture
  71. Supplier selection models using fuzzy hybrid methods in the clothing textile industry
https://doi.org/10.1515/aut-2023-0005
Keywords for this article
She nationality clothing; color feature fusion; PSO-SVM; clothing recognition
Creative Commons
BY 4.0

Articles in the same Issue

  2. Characterization of viscoelastic properties of yarn materials: Dynamic mechanical analysis in the transversal direction
  3. Analysis of omni-channel implementations that are preferred by consumers in clothing sector
  4. Structural modeling and analysis of three-dimensional cross-linked braided preforms
  5. An experimental study of mechanical properties and comfortability of knitted imitation woven shirt fabrics
  6. Technology integration to promote circular economy transformation of the garment industry: a systematic literature review
  7. Research on T-shirt-style design based on Kansei image using back-propagation neural networks
  8. Research on She nationality clothing recognition based on color feature fusion with PSO-SVM
  9. Accuracy prediction of wearable flexible smart gloves
  10. Preparation and performance of stainless steel fiber/Lyocell fiber-blended weft-knitted fabric
  11. Development of an emotional response model for hospital gown design using structural equation modeling
  12. Preparation and properties of stainless steel filament/pure cotton woven fabric
  13. Facemask comfort enhancement with graphene oxide from recovered carbon waste tyres
  14. Use of enzymatic processes in the tanning of leather materials
  15. Optical-related properties and characterization of some textile fibers using near-infrared spectroscopy
  16. Network modeling of aesthetic effect for Chinese Yue Opera costume simulation images
  17. Predicting consumers’ garment fit satisfactions by using machine learning
  18. Non-destructive identification of wool and cashmere fibers based on improved LDA using NIR spectroscopy
  19. Study on the relationship between structure and moisturizing performance of seamless knitted fabrics of protein fibers for autumn and winter
  20. Antibacterial and yellowing performances of sports underwear fabric with polyamide/silver ion polyurethane filaments
  21. Numerical and experimental analysis of ballistic performance in hybrid soft armours composed of para-aramid triaxial and biaxial woven fabrics
  22. Phonetic smart clothing design based on gender awareness education for preschoolers
  23. Determination of anthropometric measurements and their application in the development of clothing sizing systems for women in the regions of the Republic of Croatia
  24. Research on optimal design of pleated cheongsam based on Kano–HOQ–Pugh model
  25. Numerical investigation of weaving machine heald shaft new design using composite material to improve its performance
  26. Corrigendum to “Use of enzymatic processes in the tanning of leather materials”
  27. Shaping of thermal protective properties of basalt fabric-based composites by direct surface modification using magnetron sputtering technique
  28. Numerical modeling of the heat flow component of the composite developed on the basis of basalt fabric
  29. Weft insertion guideway design based on high-temperature superconducting levitation
  30. Ultrasonic-assisted alkali hydrolysis of polyethylene terephthalate fabric and its effect on the microstructure and dyeing properties of fibers
  31. Comparative study on physical properties of bio-based PA56 fibers and wearability of their fabrics
  32. Investigation of the bias tape roll change time efficiency in garment factories
  33. Analysis of foot 3D scans of boys from Polish population
  34. Optimization of garment sewing operation standard minute value prediction using an IPSO-BP neural network
  35. Influence of repeated switching of current through contacts made of electroconductive fabrics on their resistance
  36. Numerical calculation of air permeability of warp-knitted jacquard spacer shoe-upper materials based on CFD
  37. Compact Spinning with Different Fibre Types: An Experimental Investigation on Yarn Properties in the Condensing Zone with 3D-Printed Guiding Device
  38. Modeling of virtual clothing and its contact with the human body
  39. Advances in personalized modelling and virtual display of ethnic clothing for intelligent customization
  40. Investigation of weave influence on flame retardancy of jute fabrics
  41. Balloonless spinning spindle head shape optimisation
  42. Research on 3D simulation design and dynamic virtual display of clothing flexible body
  43. Turkish textile and clothing SMEs: Importance of organizational learning, digitalization, and internationalization
  44. Corrigendum To: “Washing characterization of compression socks”
  45. Study on the promotion multiple of blood flow velocity on human epidermal microcirculation of volcanic rock polymer fiber seamless knitted fabric
  46. Bending properties and numerical analysis of nonorthogonal woven composites
  47. Bringing the queen mother of the west to life: Digital reconstruction and analysis of Taoist Celestial Beings Worshiping mural’s apparel
  48. Modeling process for full forming sports underwear
  49. Retraction of: Ionic crosslinking of cotton
  50. An observational study of female body shape characteristics in multiracial Malaysia
  51. Study on theoretical model and actual deformation of weft-knitted transfer loop based on particle constraint
  52. Design and 3D simulation of weft-knitted jacquard plush fabrics
  53. An overview of technological challenges in implementing the digital product passport in the textile and clothing industry
  54. Understanding and addressing the water footprint in the textile sector: A review
  55. Determinants of location changes in the clothing industry in Poland
  56. Influence of cam profile errors in a modulator on the dynamic response of the heald frame
  57. Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy
  58. 3D simulation of double-needle bar warp-knitted clustered pile fabrics on DFS
  59. Finite element analysis of heat transfer behavior in glass fiber/metal composite materials under constant heat load
  60. Price estimation and visual evaluation of actual white fabrics used for dress shirts and their photographic images
  61. Effect of gluing garment materials with adhesive inserts on their multidirectional drape and bending rigidity
  62. Optimization analysis of carrier-track collision in braiding process
  63. Numerical and experimental analysis of the ballistic performance of soft bulletproof vests for women
  64. The antimicrobial potential of plant-based natural dyes for textile dyeing: A systematic review using prisma
  65. Influence of sewing parameters on the skin–fabric friction
  66. Validation by experimental study the relationship between fabric tensile strength and weave structures
  67. Optimization of fabric’s tensile strength and bagging deformation using surface response and finite element in stenter machine
  68. Analysis of lean manufacturing waste in the process flow of ready-to-wear garment production in Nigeria
  69. An optimization study on the sol–gel process to obtain multifunctional denim fabrics
  70. Drape test of fully formed knitted flared skirts based on 3D-printed human body posture
  71. Supplier selection models using fuzzy hybrid methods in the clothing textile industry
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 6.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/aut-2023-0005/html
Scroll to top button