Search Results (13)

In this paper, a comparative analysis of historical and contemporary fashion designs was conducted using quantitative methods and indices. Elements such as silhouettes, color palettes, and structural characteristics were analyzed in order to identify models for reinterpretation of classic fashion costume. Clothing from four historical periods was studied: Empire, Romanticism, the Victorian era, and Art Nouveau. An image processing algorithm was proposed, through which data on the shapes and colors of historical and contemporary clothing were obtained from digital color images. The most informative of the shape and color indices of contemporary and historical clothing were selected using the RReliefF, FSRNCA, and SFCPP methods. The feature vectors were reduced using the latent variable and t-SNE methods. The obtained data were used to group the clothing according to historical periods. Using Euclidean distances, the relationship between clothing by contemporary designers and the elements of the historical costume used by them was determined. These results were used to create an educational and methodological framework for practical training of students in the field of fashion design. The results of this work can help contemporary designers in interpreting and integrating elements of historical fashion into their collections, adapting them to the needs and preferences of consumers. Full article

In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness. Full article

The fashion industry has a critical need for fashion compatibility. Modeling compatibility is a challenging task that involves extracting (in)compatible features of pairs, obtaining compatible relationships between matching items, and applying them to personalized recommendation tasks. Measuring compatibility is a complex and subjective concept in general. The complexity is reflected in the fact that relationships between fashion items are determined by multiple matching rules, such as color, shape, and material. Each personal aesthetic style and fashion preference differs, adding subjectivity to the compatibility concept. As a result, personalized factors must be considered. Previous works mainly utilize a convolutional neural network to measure compatibility by extracting general features, but they ignore fine-grained compatibility features and only model overall compatibility. We propose a novel neural network framework called the Attention-based Personalized Compatibility Embedding Network (PCE-Net). It comprises two components: attention-based compatibility embedding modeling and attention-based personal preference modeling. In the second part, we utilize matrix factorization and content-based features to obtain user preferences. Both pieces are jointly trained using the BPR framework in an end-to-end method. Extensive experiments on the IQON3000 dataset demonstrate that PCE-Net significantly outperforms most baseline methods. Full article

The textile industry is one of the most complex sectors, in terms of the materials and chemical processes used from petroleum and the environmental degradation during its production and disposal. It is therefore a sector looking for new possibilities and for more sustainable materials and applications. One option is to use natural dyes, as they are considered biodegradable, do not pollute the environment, and have potential use for many sectors, including the fashion industry. In this study, Alanya silk was dyed by a natural dyeing method with crocus sativus, Helichrysum arenarium, and Glycyrrhiza glabra L., plants that grow in and around the Alanya region. Quercus aegilops L. grown in the region was preferred as mordant, a natural binder, and is one of the plants with the highest tannin content, and it was used with a more environmentally friendly and sustainable approach to increase the binding in natural dyeing instead of chemical mordants. The aim is to provide an environmental and scientific contribution to the dyeing producers in this region. According to the MAUT (Multi-Attribute Utility Theory) method, the best dyes in terms of fastness and color efficiency were determined as the dyes made with the Glycyrrhiza glabra L. plant. Full article

The start of the consumer society in Norway is examined by studying the increased imports of colorants and their constituents during the 18th century. Based on historical customs records, 82 imported pigments and dyes, 27 binders and additives and nine mordants and auxiliaries are presented. Imports increased significantly in the middle and at end of the century, representing two chromatic “revolutions”. This was especially evident for lead white and indigo; being the only particularly white and blue pigments used for painting and dyeing, respectively. Red dyes at different prices and properties (brazilwood, madder and cochineal) met the demands for red textile coloring in different social groups. The study presents a comprehensive overview of colorant imports and provides new insights in the development of consumption in Norway. Colorant imports were probably initiated by a supply-driven positive feed-back loop as a result of increased export trade. This was followed by a demand-driven loop, involving increased domestic trade, product preferences, “fashionability”, consumer culture, economic conditions and enlightenment. A model is presented that can contribute to a further understanding of the start of the consumer society in the second half of the 18th century in Norway. Full article

Owing to high competition in e-commerce, customers may prefer sites that ensure that good user experience (UX) and website aesthetics are one of its qualities. The method of presenting items seems crucial for gaining and maintaining user attention. We conducted a task-based user eye-tracking study with n = 30 participants to examine two variants of an online fashion store: one based on aesthetic rules and one defying them. The following aspects of item presentation were considered: height and width the ratio of product photos, website colors, rounded borders, text visibility, spacing between elements, and smooth animation. We investigated their relationship to user attention by analyzing gaze fixation, tracking user interest, and conducting a supplementary survey. Experimental results showed that owing to following the rules of aesthetics in interface design in the presented fashion shopping scenario, elements such as the recommendation area and product highlights had a significant positive impact on customer attention. Full article

Given the potentially significant environmental impacts of fashion design, various design approaches are required to extend product lifespan. Digital design methods may play an essential role in reducing the environmental impact of products and production processes. In addition, a design approach inspired by nature, where humans have long lived, is valid for sustainable design innovation. The purpose of this study is to examine the aesthetics of Koreans, who prefer nature, and to find a sustainable fashion design approach by using it as a knowledge database. In this study, a parametric design methodology that can reflect knowledge-based data in the process of producing 3D printing sustainable fashion products, considering the emotional durability of consumers, was used. The study results are as follows. From the aesthetic point of view of Korea, sustainable design characteristics represent unique Korean folk art, resilience to nature, and simplicity that resembles nature. The properties of the form represented to “forms resembling nature”, “changeable forms”, “organic forms”, and “minimal forms”. Materials were “nature inspired textures”, “rustic natural materials”, and “regional materials”. Colors were “the colors of nature” and “indigenous colors”. The parametric controls variables used for 3D printing the fashion products were size, assembly style, and sustainable material. These control parameters were used to create designs according to the individual taste of users. In the 3D printing fashion product design process, pieces were printed in different shapes and sizes by controlling the parameters to create designs according to users’ tastes and Korean aesthetics. It was determined that this process could extend the lifespan of products, and that it is possible to modify sustainable fashion products according to personal taste by adjusting numerical values and extracting visual images based on knowledge of art and culture. Full article

This study aimed to investigate consumers’ visual image evaluation of wrist wearables based on Kansei engineering. A total of 8 representative samples were screened from 99 samples using the multidimensional scaling (MDS) method. Five groups of adjectives were identified to allow participants to express their visual impressions of wrist wearable devices through a questionnaire survey and factor analysis. The evaluation of eight samples using the five groups of adjectives was analyzed utilizing the triangle fuzzy theory. The results showed a relatively different evaluation of the eight samples in the groups of “fashionable and individual” and “rational and decent”, but little distinction in the groups of “practical and durable”, “modern and smart” and “convenient and multiple”. Furthermore, wrist wearables with a shape close to a traditional watch dial (round), with a bezel and mechanical buttons (moderate complexity) and asymmetric forms received a higher evaluation. The acceptance of square- and elliptical-shaped wrist wearables was relatively low. Among the square- and rectangular-shaped wrist wearables, the greater the curvature of the chamfer, the higher the acceptance. Apparent contrast between the color of the screen and the casing had good acceptance. The influence of display size on consumer evaluations was relatively small. Similar results were obtained in the evaluation of preferences and willingness to purchase. The results of this study objectively and effectively reflect consumers’ evaluation and potential demand for the visual images of wrist wearables and provide a reference for designers and industry professionals. Full article

With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching. Full article

When it comes to pollution, we do not usually think about the clothes we wear, but the clothing industry is really endangering our planet. The market economy has transferred the decision-making power of the garment industry from enterprises to consumers. To make the fashion industry sustainable, in addition to technological innovation, it is also necessary to conduct research on the service objects of the industry. Consumer clothing preference research is an important part of the sustainable development of the clothing industry, and it will also have an impact on environmental and design sustainability. Hence, a psychophysical experiment based on solid color shirts is carried out to analyze people’s perceptions and preferences concerning fashion colors, including the aesthetic differences and similarities between males and females, and establish a hierarchical feed-forward model of color preferences relating to solid color shirts. Firstly, 480 colors of solid shirts from different clothing brands were collected, and the mean shift clustering algorithm was used to classify them into 19 clusters in the CIELAB color space. Secondly, another 22 solid colors, combined with the 19 colors of the cluster centers, formed a solid color scheme. Thirdly, 41 solid male and female shirts and fabrics were simulated as stimuli in three dimensions, and they were presented on a calibrated computer display. The simulations were assessed by 34 observers (consisting of 17 males and 17 females) in terms of 11 semantic scales, including cold/warm, heavy/light, passive/active, dirty/clean, tense/relaxed, plain/gaudy, traditional/modern, masculine/feminine, slim-look/fat-look, hard-to-match/easy-to-match, and dislike/tike. The experimental results demonstrated that the hard-to-match/easy-to-match response was found to be highly correlated with dislike/like. Furthermore, the response of the females concerning hard-to-match/easy-to-match had a strong correlation with two adjective pairs (plain/gaudy and slim-look/fat-look), while that of the males also had a strong correlation with two adjective pairs (plain/ gaudy and masculine/feminine). Finally, a hierarchical feed-forward model of aesthetic perception for solid color shirts was established to predict the shirt preference degree. These findings could be used to develop a more robust and comprehensive theory of fashion color preferences and provide a reference for the design of solid color shirts. A more comprehensive color preference theory is not only an effective tool to solve the problem of pollution in the clothing industry, but also an important theoretical basis for the “sustainable design” of clothing, which is of great significance to the sustainable development of the clothing industry. Full article

Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is especially true in fashion design and form design of many other types of products. In this paper, we propose to combine Kansei engineering and the deep learning for product innovation (KENPI) framework, which can transfer color, pattern, etc. of a style image in real time to a product’s shape automatically. To capture user preferences, we combine Kansei engineering with back-propagation neural networks to establish a mapping model between product properties and styles. To address the inspiration issue in product innovation, the convolutional neural network-based neural style transfer is adopted to reconstruct and merge color and pattern features of the style image, which are then migrated to the target product. The generated new product image can not only preserve the shape of the target product but also have the features of the style image. The Kansei analysis shows that the semantics of the new product have been enhanced on the basis of the target product, which means that the new product design can better meet the needs of users. Finally, implementation of this proposed method is demonstrated in detail through a case study of female coat design. Full article

The market economy has shifted the decision-making power of the garment industry from the enterprise to the consumer. Research on consumer clothing preferences is an essential part of sustainable development of the garment industry. Based on data statistics from eight fast fashion brands, black and white are most commonly used in two-color plaid shirts. This paper carried out a psychophysical experiment to investigate factors affecting pattern preferences for black-and-white shirts and the differences and similarities between male and female pattern preferences. Twenty-eight different representative patterns of plaid shirts were selected by five fashion designers together from 190 different black-and-white plaid shirts from eight fast fashion brands, which were then classified into three categories: gingham, tartans, and windowpane. Based on these patterns, 28 male and female shirts were simulated in three dimensions and presented on a calibrated computer display. The simulations were assessed by 42 observers (consisting of 21 males and 21 females) in terms of four semantic scales, including light–dark, delicate–rough, simple–complex, and like–dislike. The experimental results revealed that there was no significant difference of pattern preference between females and males for 89.29% of the black-and-white plaid shirts, and also described features of the patterns that the females and males liked or disliked. Furthermore, the study also demonstrated the formulation between the four semantic scales and three pattern features (including the percentage of black region, the size of the minimum repeat unit, and the descriptor of the pattern complexity). The findings could be used to develop a more robust and comprehensive theory of pattern preferences and provide a reference for pattern design for black-and-white plaid shirts. More comprehensive pattern preference theory is not only an effective tool to solve the problem of plaid shirt inventory in the garment industry but also an important theoretical basis for the “sustainable design” of clothing, which is of great significance to the sustainable development of the garment industry. Full article