Fabric Image Research Articles

Enhancing consistency in virtual try-on: A novel diffusion-based approach

In new technology scenarios, virtual try-on aims to integrate clothing onto body images naturally, enhancing shopping experience by simulating true effect of clothes. As the image resolution increases, we expect to improve the consistency of the result, i.e., to ensure that various elements of the image are harmonized in terms of color, shading, style, and texture in order to achieve a natural visual effect. Many studies based on Generative Adversarial Networks(GANs) struggle with consistency. They encounter challenges in accurately depicting the fabric of target garments, as well as natural shadows and folds, and sometimes exhibit visual discontinuities or inconsistencies. So, we propose a new approach CSD-VTON based on latent diffusion model. Considering that traditional UNet's computational primitives struggle to capture complex transformation relationships at the pixel level, we address this issue by concatenating the warped cloth images generated by the warping module with the noise image. Additionally, cascade feature extraction module is introduced to extract in-store garment features, which ensures the preservation of texture and details in the target garments. Finally, we incorporate the skip-connection supplementary module to compensate for the reconstruction error. We conducted experiments using the DressCode and VITON-HD datasets to demonstrate the effectiveness and superiority of our approach.

Mechanical strength recognition and classification of thermal protective fabric images after thermal aging based on deep learning

Objectives. Currently, numerous studies have focused on testing or modeling to evaluate the safe service life of thermal protective clothing after thermal aging, reducing the risk to occupational personnel. However, testing will render the garment unsuitable for subsequent use and a series of input parameters for modeling are not readily available. In this study, a novel image recognition strategy was proposed to discriminate the mechanical strength of thermal protective fabric after thermal aging based on transfer learning. Methods. Data augmentation was used to overcome the shortcoming of insufficient training samples. Four pre-trained models were used to explore their performance in three sample classification modes. Results. The experimental results show that the VGG-19 model achieves the best performance in the three-classification mode (accuracy = 91%). The model was more accurate in identifying fabric samples in the early and late stages of strength decline. For fabric samples in the middle stage of strength decline, the three-classification mode was better than the four-classification and six-classification modes. Conclusions. The findings provide novel insights into the image-based mechanical strength evaluation of thermal protective fabrics after aging.

Graph relationship-driven label coded mapping and compensation for multi-label textile fiber recognition

Recently, neural network-based methods for multi-label textile fiber recognition have achieved considerable success. However, a significant limitation of most current approaches is their disregard for the valuable dependencies that exist among different fiber categories. The universal multi-label image recognition methods that consider label relationships often fall short when applied to the challenge posed by the mixture of fibers in textile images. And these relationship modeling manners have not yet been used in the field of multi-label textile fiber recognition. In this work, a graph relationship-driven method is proposed for the recognition of multi-label textile fibers. Based on the graph attention network, the proposed method introduces global relation graph, label coded mapping, and label compensation to equip the feature learning backbone with the relationship modeling ability. First, feature learning backbone extracts the semantic features from images, and global relation graph mines shallow representations of global label relationships from label embeddings. Then, label coded mapping combines these semantic features and global features to model joint relationships. The obtained joint representations constitute the label code, which is used to map the fiber class indices. Finally, label compensation further extracts the deep representations of global label relationships and utilizes them to compensate the fiber class indices to obtain final prediction scores. By employing experiments on a textile fiber dataset and a rearranged dataset, the effectiveness and superiority of the proposed method are validated. Further experiments on PASCAL VOC 2007 showcase the potential applications of the proposed method beyond textile fiber recognition.

Fabric pilling image segmentation by embedding dual-attention mechanism U-Net network

The initial step in fabric pilling rating is to segment and localize the pilling region, which is a crucial and challenging task. This paper presents a fabric puckering image segmentation method that is integrated into a U-Net network with a dual-attention mechanism. We have enhanced the fully convolutional neural network (U-Net) model by incorporating the dual-attention mechanism. This modification has resulted in a powerful feature extraction capability, enabling the objective and accurate segmentation of the fabric puckering region. We refer to this improved model as the dual-attention U-Net. The network model for fabric pilling feature extraction adopts the improved VGG16 model architecture as its encoding part. The model parameters are initialized with VGG16 pre-training weights to accelerate convergence speed. Second, the feature fusion between the corresponding layers of the encoding part and the decoding part was fed into the dual-attention mechanism module to strengthen the weight values of the fabric pilling region adaptively, which made the model more focused on the fabric pilling target region; Third, the dual-attention U-Net model was trained by data augmentation and migration learning strategies to prevent overfitting; Finally, the performance of the model was evaluated with the collected fabric pilling data set. The results of the experiments indicate that the claimed dual-attention U-Net model is superior to the typical U-Net model in terms of accuracy and precision. The dual-attention U-Net model achieved 99.03% accuracy for fabric pilling segmentation.

Chi-Square Histogram Analysis of Woven Fabric Images Made from Natural Dyes Due to Exposure to Sunlight

Chi-Square Histogram Analysis of Woven Fabric Images Made from Natural Dyes Due to Exposure to Sunlight

Color Compensation Method of 2-D Clothing Image Based on Visual Communication from the Perspective of Internet of Things

Abstract From the perspective of the Internet of Things, market competition is becoming increasingly fierce, and Internet of Things technology can provide more possibilities for clothing brands’ advertising and marketing strategies, further shaping brand image. However, the color of 2-D clothing images is imbalanced. Therefore, when using the current method for color compensation processing of 2-D clothing images, it is prone to interference from issues such as lighting shadows, uneven brightness, and angle changes. The structural similarity of 2-D clothing images is low, the peak signal-to-noise ratio (PSNR) is low, the standard deviation is low, and the visual effect is poor. The research goal is to solve this problem, and a color compensation method of 2-D clothing image based on visual communication from the perspective of the Internet of Things is proposed. Firstly, the 2-D clothing image is segmented by histogram of oriented gradients feature and exemplar support vector machine classifier, and then the dimension of the image is reduced by weighted subspace probabilistic clustering analysis(WSPCA) algorithm and nonlinear algorithm, and the noise in the image is eliminated by homomorphic filtering method. Finally, the preprocessed image is input into a Gaussian homomorphic filter to complete the color compensation of the 2-D clothing image. Analyzing the experimental results, it can be seen that the proposed algorithm has high structural similarity, high PSNR, high standard deviation, and good visual effect.

DESIGN OF CORPORATE CLOTHING: AESTHETIC AND ERGONOMIC ASPECTS

The article is devoted to the process of designing corporate clothing for employees and students of higher education of the Khmelnytskyi National University, which is considered as a system of "corporate style → corporate image → corporate clothing and its identity", including logos, colors, graphic elements and graphic standards of visual identification of the KhNU and their structure. Scientific novelty. The most characteristic artistic and compositional solutions and the typology of graphic elements used in the corporate style of KhNU as a means of communication for the creation of the image and brand of the higher education institution were analyzed and highlighted. Practical meaning. The developed systematization of the graphic elements of the corporate style of KhNU made it possible to generalize their artistic and compositional features and use them in the design of men's and women's corporate clothing for employees and students of higher education of the Khmelnytskyi National University for visual representation and broadcasting of the values and main characteristics of the educational institution. The process of creating a sketch and harmonizing the project image of corporate clothing with the help of an information model, which allows to present it as a single symbolic system with the help of modern practices of visual presentation of fashion innovations, is demonstrated. The design stages of corporate clothing samples are presented with the help of computer technologies, which provide clothing designers with wide visual and artistic opportunities in the context of modern communication practices. Artistic and design, constructive and technological solutions for men's and women's corporate clothing models were developed, which were produced under the conditions of the Ukrainian fashion brand ARTY Sport using a calender thermopress for sublimation. Testing of experimental samples of products took place at the exhibition dedicated to the Day of Science (Khmelnytskyi, KhNU, 2022). Research materials can be used in the development of corporate style and design of modern corporate clothing. The results of the study can be used in further scientific research on the design of other types of corporate clothing for employees and students of higher education of the Khmelnytskyi National University.

Reliable fabric defect detection via Bayesian uncertainty modeling

Despite the demonstrated capability of deep learning models in detecting anomalies in textile images, their predictions in real-world applications tend to be overly confident, especially when faced with defect types not previously encountered in the training set or when dealing with low-quality annotations. This excessive confidence in predictions limits the practical application of deep learning methods in textile defect detection, as it fails to provide inspectors with reliable guidance on when to trust the model's predictions and when manual verification is necessary. To address this issue, this paper introduces a Bayesian fabric anomaly detection model that utilizes Variational Inference (VI) to apply Bayesian inference to the widely used U-Net architecture. During the inference phase, the model employs Monte Carlo sampling to perform multiple forward passes, generating three types of uncertainty estimations and per-pixel uncertainty maps, thus providing comprehensive evidence for decision-making. This method not only estimates the uncertainty of model predictions but also improves the F1 score by 2-4% over the baseline U-Net model in the frequency domain. This study proves the Bayesian approach boosts fabric anomaly detection and decision-making by optimizing model performance and reducing reliance on inaccurate predictions.

A superpixel‐based color difference detection algorithm for dyed fabrics

AbstractIn order to enhance the accuracy and efficiency of color difference detection for dyed fabrics, this article introduces a novel superpixel‐based color difference detection algorithm, which exhibits three significant improvements over the previous algorithm. First, it proposes a more rational color feature extraction method to extract more detailed color features from fabric images, which uses superpixels as basic units for both color feature extraction and color difference detection. Second, the algorithm employs the CIEDE2000 color difference formula to calculate color difference values. This eliminates the need for obtaining standard fabric images as references in advance, making the process more convenient and practical. Finally, an area‐based color difference calculation method is proposed based on the human visual mechanism, ensuring that the evaluation results are more consistent with human eye observations. Experimental results demonstrate that the proposed algorithm provides more accurate and visually consistent color difference assessments compared to the previous algorithm.

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

Personalized Clothing Prediction Algorithm Based on Multi-modal Feature Fusion

With the popularization of information technology and the improvement of material living standards, fashion consumers are faced with the daunting challenge of making informed choices from massive amounts of data. This study aims to propose deep learning technology and sales data to analyze the personalized preference characteristics of fashion consumers and predict fashion clothing categories, thus empowering consumers to make well-informed decisions. The Visuelle’s dataset includes 5,355 apparel products and 45 MB of sales data, and it encompasses image data, text attributes, and time series data. The paper proposes a novel 1DCNN-2DCNN deep convolutional neural network model for the multi-modal fusion of clothing images and sales text data. The experimental findings exhibit the remarkable performance of the proposed model, with accuracy, recall, F1 score, macro average, and weighted average metrics achieving 99.59%, 99.60%, 98.01%, 98.04%, and 98.00%, respectively. Analysis of four hybrid models highlights the superiority of this model in addressing personalized preferences.

Diverse Person: Customize Your Own Dataset for Text-Based Person Search

Text-based person search is a challenging task aimed at locating specific target pedestrians through text descriptions. Recent advancements have been made in this field, but there remains a deficiency in datasets tailored for text-based person search. The creation of new, real-world datasets is hindered by concerns such as the risk of pedestrian privacy leakage and the substantial costs of annotation. In this paper, we introduce a framework, named Diverse Person (DP), to achieve efficient and high-quality text-based person search data generation without involving privacy concerns. Specifically, we propose to leverage available images of clothing and accessories as reference attribute images to edit the original dataset images through diffusion models. Additionally, we employ a Large Language Model (LLM) to produce annotations that are both high in quality and stylistically consistent with those found in real-world datasets. Extensive experimental results demonstrate that the baseline models trained with our DP can achieve new state-of-the-art results on three public datasets, with performance improvements up to 4.82%, 2.15%, and 2.28% on CUHK-PEDES, ICFG-PEDES, and RSTPReid in terms of Rank-1 accuracy, respectively.

FabricGAN: an enhanced generative adversarial network for data augmentation and improved fabric defect detection

When deep learning is applied to intelligent textile defect detection, the insufficient training data may result in low accuracy and poor adaptability of varying defect types of the trained defect model. To address the above problem, an enhanced generative adversarial network for data augmentation and improved fabric defect detection was proposed. Firstly, the dataset is preprocessed to generate defect localization maps, which are combined with non-defective fabric images and input into the network for training, which helps to better extract defect features. In addition, by utilizing a Double U-Net network, the fusion of defects and textures is enhanced. Next, random noise and the multi-head attention mechanism are introduced to improve the model’s generalization ability and enhance the realism and diversity of the generated images. Finally, we merge the newly generated defect image data with the original defect data to realize the data enhancement. Comparison experiments were performed using the YOLOv3 object detection model on the training data before and after data enhancement. The experimental results show a significant accuracy improvement for five defect types – float, line, knot, hole, and stain – increasing from 41%, 44%, 38%, 42%, and 41% to 78%, 76%, 72%, 67%, and 64%, respectively.

Fabric defect image generation method based on the dual-stage W-net generative adversarial network

Due to the intricate and diverse nature of textile defects, detecting them poses an exceptionally challenging task. In comparison with conventional defect detection methods, deep learning-based defect detection methods generally exhibit superior precision. However, utilizing deep learning for defect detection requires a substantial volume of training data, which can be particularly challenging to accumulate for textile flaws. To augment the fabric defect dataset and enhance fabric defect detection accuracy, we propose a fabric defect image generation method based on Pix2Pix generative adversarial network. This approach devises a novel dual-stage W-net generative adversarial network. By increasing the network depth, this model can effectively extract intricate textile image features, thereby enhancing its ability to expand information sharing capacity. The dual-stage W-net generative adversarial network allows generating desired defects on defect-free textile images. We conduct quality assessment of the generated fabric defect images resulting in peak signal-to-noise ratio and structural similarity values exceeding 30 and 0.930, respectively, and a learned perceptual image patch similarity value no greater than 0.085, demonstrating the effectiveness of fabric defect data augmentation. The effectiveness of dual-stage W-net generative adversarial network is established through multiple comparative experiments evaluating the generated images. By examining the detection performance before and after data augmentation, the results demonstrate that mean average precision improves by 6.13% and 14.57% on YOLO V5 and faster recurrent convolutional neural networks detection models, respectively.

Optimization of Canny Algorithm for Recognition of Female Clothing Styles from the Perspective of Features

The accurate and fast recognition of women's clothing styles is conducive to the classification and recommendation of merchants, and it is also convenient for customers to choose from. This paper optimized the Canny algorithm used to extract the contour edge of the female clothing image and used a convolutional neural network (CNN) classifier to identify clothing styles based on the edges extracted by the Canny algorithm. Finally, the Canny algorithm and the CNN classifier were tested in the simulation experiment. The performance of the CNN classifier was compared with that of the template matching and SVM classifiers, the Resnet34-based recognition method, as well as the target detection network and genetic algorithm-back-propagation neural network combined recognition method. The results demonstrated that the optimized Canny algorithm extracted more distinct contour edges. The CNN classifier exhibited the best performance and the fastest recognition for female clothing styles.

Clothing Style Similarity Matching and Classification Recognition Algorithm Based on Double-Layer Model in the Context of the Internet of Things

Abstract The identification of different clothing style attributes is helpful for designers to grasp the clothing style, and it is also helpful for consumers to find clothes with the same style attributes according to their own preferences. This is becoming more important in the clothing design, Internet, and e-commerce industries. It is of great significance to carry out clothing style similarity matching and classification recognition. For the problem of clothing style similarity matching and classification recognition, the traditional algorithm stays in the stage of qualitative analysis and subjective evaluation, unable to quantitatively and objectively determine clothing style, resulting in the decline of clothing style similarity matching effect and classification recognition accuracy. Therefore, a similarity matching, classification, and recognition algorithm of clothing style based on the double-layer model in the context of the Internet of Things is designed. The double-layer model is constructed through the target detection layer and target segmentation layer. The double-layer model is used to realize the segmentation of clothing image. The overall similarity evaluation index of image style is obtained according to the image gradient to complete the similarity matching of clothing style. On this basis, the clothing image features are extracted and input into the support vector machine classifier to complete the clothing classification and recognition. The experimental results show that the proposed garment style similarity matching and classification recognition algorithm based on the two-layer model has high image segmentation accuracy, good style similarity matching effect, high classification recognition accuracy, and high efficiency, which proves that the algorithm is feasible for garment style similarity matching and classification recognition using the two-layer model and can be further applied in the field of garment design.

Image retrieval of colored spun fabrics based on variable weight and semantic features

Multi-level features of images have different contributions to image retrieval. Fixed weight feature fusion cannot give full play to the effectiveness of shallow visual features and semantic features in image retrieval of colored spun fabrics. In view of this fact, this paper proposes an algorithm of colored spun fabrics image retrieval on variable weight and semantic features. The algorithm constructs the similarity correlation coefficient based on the contribution of features at different levels to image retrieval, and obtains the fusion weight coefficient of multi-level features by retrieving the change rate of similarity score of the images and k neighbor images in the sample base. Experimental results show that the proposed algorithm on variable weight feature fusion can bring all features into full play. Compared with the retrieval method that only based on the shallow visual features fusion or the fixed weight feature fusion, the Recall rate of the proposed algorithm increases by 13.15% and 0.54% respectively, and the mean Average Precision of the whole class increases by 7.21% and 1.15% respectively. This research can provide technical support for digital management and product development of colored spun fabrics image, and it has important theoretical research value and broad application prospects.

Retracted: Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

Retracted: Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

Automatic fabric defect detection in textile images using a labview based multiclass classification approach

Automatic fabric defect detection in textile images using a labview based multiclass classification approach

Fabric image retrieval based on multi-modal feature fusion

Fabric image retrieval based on multi-modal feature fusion