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Clothing retrieval is very useful to help the clients to efficiently search out the apparel they want. Currently, the mainstream clothing retrieval methods are attribute semantics based, which however are inconvenient for common clients. The purpose of this paper is to provide an easy‐to‐operate apparels retrieval mode with the authors' novel approach of clothing image similarity measurement.

The authors measure the similarity between two clothing images by computing the weighted similarities between their bundled features. Each bundled feature consists of the point features (SIFT) which are further quantified into local visual words in a maximally stable extremal region (MSER). The authors weight the importance of bundled features by the precision of SIFT quantification and local word frequency that reflects the frequency of the common visual words appeared in two bundled features. The bundled features similarity is computed from two aspects: local word frequency; and SIFTs distance matrix that records the distances between every two SIFTs in a bundled feature.

Local word frequencies improves the recognition between two bundled features with the same common visual words but different local word frequency. SIFTs distance matrix has the merits of scale invariance and rotation invariance. Experimental results show that this approach works well in the situations with large clothing deformation, background exchange and part hidden, etc. And the similarity measurement of Weight+Bundled+LWF+SDM is the best.

This paper presents an apparel retrieval mode based on local visual features, and presents a new algorithm for bundled feature matching and apparel similarity measurement.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.

From the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.

The objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.

The paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.

Exercising is a key approach adopted by muscular dystrophy patients to halt the weakening of muscles as it can eventually lead to serious immobility issues. Though it is essential to exercise on a daily basis for healthy living, there is no mention of any research effort in the current literature regarding the development of an apparel product for these mobility-affected patients that might assist them both in meeting their exercising needs and providing them some comfort in their daily living. Thus, this paper aims to focus on identifying the specific needs of muscular dystrophy victims and proposing special adaptive clothing solutions to support their daily exercise and mobility needs.

To achieve the objectives of this study, attention was focused on the muscular dystrophy afflicted women in Sri Lanka. Semi-structured interviews were conducted with the female victims of muscular dystrophy and their lifestyles were observed carefully; additional data were gathered by holding semi-structured interviews with their physiotherapists. Further, interviews were conducted with both garment technologists and fabric technologists too. Data gathered through these methods were analyzed qualitatively using the principles of thematic analysis and then aggregate conclusions were drawn.

It was observed that the patients were engaged in special activities such as exercising three times a day besides following their normal day-to-day activities to maintain and develop muscle strength. It soon became evident that these women found it difficult to perform their daily exercise routines with their regular clothing and were looking for custom made clothing they could wear all day long in comfort and avoid the problems that arose while exercising. The study specifies the requirements that must be met to satisfy both generic and specific needs. Considering all these aspects some adaptive clothing solutions were proposed to support daily exercising activity with respect to comfort, convenience, health and safety, as well as socio-cultural and psychological needs.

The area of fusing generic and specific features to support the daily exercising needs of muscular dystrophy victims is an untouched field of experimentation and being a need of the disabled, the present study marks a milestone on the way to a novel area of apparel design, besides exploring a new field of research.

The purpose of this paper is to study online clothing consumers' behaviour and their visual attention mechanism to provide objective and quantitative evidences for the display and sales of online clothing.

Firstly, this paper conducted a Focus Group Methodology and questionnaire survey to obtain concern factors of online clothing. Secondly, the online clothing's bottom-up visual stimulation and consumer's top-down expectations were analysed, and proposed the hypotheses about significant stimulus of clothing and consumer's emotional experience. Thirdly, the online clothing consumer's visual attention rules and related qualitative results were discussed, and proposed visual attention law for online clothing. Finally, took the company's 84th quarter clothing design practices as research projects, all the hypotheses were demonstrated through eye movement physiology experiments, online clothing trial release and node sales data.

Online clothing has unique visual display ways compared with other online products such as online advertising, brands and food packaging. Clothing patterns of unfamiliar (fresh) font shapes are more attractive than the patterns of familiar fonts. The cause of the bottom-up visual attention bias is the contrast between clothing features, not the absolute stimulus intensity of the features themselves. Clothing factors can change their emotional experience from no difference to significant difference under the influence of other clothing factors.

Put forward hypotheses of online clothing consumer behaviour and its visual attention mechanism, provided objective and quantitative evidences through eye tracker.

The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge pixel-level features and high-level category labels. In order to improve the robustness accuracy of the network, six transformation methods are used to preprocess images. To avoid representational bottlenecks, small-sized convolution kernels are adopted in the network. This method first pretrains the network on ImageNet and then fine-tune the model in clothing data set.

The paper opts for an exploratory study by using the control variable comparison method. To verify the rationality of the network structure, lateral contrast experiments with common network structures such as VGG, GoogLeNet and AlexNet, and longitudinal contrast tests with different structures from one another are performed on the created clothing image data sets. The indicators of comparison include accuracy, average recall, average precise and F-1 score.

Compared with common methods, the experimental results show that the proposed network has better performance on clothing recognition. It is also can be found that larger input size can effectively improve accuracy. By analyzing the output structure of the model, the model learns a certain “rules” of human recognition clothing.

Clothing analysis and recognition is a meaningful issue, due to its potential values in many areas, including fashion design, e-commerce and retrieval system. Meanwhile, it is challenging because of the diversity of clothing appearance and background. Thus, this paper raises a network based on deep sparse CNN to realize clothing recognition.

Adjustable children’s clothing is being acknowledged as the best solution worldwide to reduce the unnecessary squandering of money and accumulation of waste materials in landfills because provision for size adjustment helps to increase the lifespan of a garment. There are no studies on the preferences of both children and their parents for adjustable children's clothing, although they are equally involved as consumers in the children’ clothing market. Thus, this paper aims to explore the preferences of children and their parents about adjustable children’s clothing.

Five children’s wear fashion outlets in the Colombo District were studied to explore the types of children’s clothing that are available in the Sri Lankan market. A questionnaire survey was conducted with parents who have children between the ages of 8 and 12 years to determine their perceptions on the use of adjustable children’s clothing. Five children living in the Colombo District were also interviewed.

According to the questionnaire survey results, 53% of the respondents who have children in the age group 8–12 years were unaware of the existence of adjustable children's clothing, whereas 47% had at least some knowledge of them. Interviews with the five children showed that only two children were aware of garments with adjustment features. As it turned out though, four children were indeed wearing them but did not realize those were adjustable garments. It was found that if adjustable children's clothing were freely available in the Sri Lankan market, 79.5% of the respondents would prefer to buy them because they could appreciate the financial and environmental benefits that would result. Clothing made of standard, high quality, durable and comfortable materials were all in demand. In addition, parents expected adjustable children's clothing products to be available at affordable prices.

This study provides important knowledge that can fill the gap in the existing literature on adjustable children’s clothing. The findings will be beneficial for the children’s clothing product designers and developers, as well as academia.

Extending the active lifetimes of garments by producing better quality is a widely discussed strategy for reducing environmental impacts of the garment industry. While quality is an important aspect of clothing, the concept of quality is ambiguous, and, moreover, consumers may perceive quality in individual ways. Therefore, it is important to deepen the general understanding regarding the quality of clothing.

This paper presents an integrated literature review of the recent discussion of perceived quality of clothing and of the links between quality and clothing lifetimes; 47 selected articles and other literature obtained primarily through fashion/clothing/apparel journals were included in this review.

The main ideas from the articles are thematized into the following sections: the process of assessment, levels involved in assessment, multidimensional cues of assessment, and quality and clothing use times. The paper highlights that perceiving quality is a process guided by both expectations and experience, and assembles the various aspects into a conceptual map that depicts the connections between the conceptual levels involved in assessing quality. It also illustrates connections between quality and clothing use times.

This paper focused on perceived quality on a conceptual level. Further studies could examine and establish deeper links between quality, sustainability and garment lifespans.

The study draws together studies on perceived quality, presenting the foundational literature and key concepts of quality of clothing. It summarizes them in a conceptual map that may help visualize various aspects affecting the assessment of quality and deepen the general understanding of the quality of garments.

This paper aims to identify the relationship between users' perception image, clothing design features and users' preference and propose a clothing design scheme based on users perception image and users' preference.

In this paper, men's suit is composed into multiple design features under the design elements. Using the orthogonal experiment method, 16 schemes of the representative suit are designed. Through perception evaluation experiment, users' perception images and preference degree of the samples are obtained. By partial least squares (PLS) analysis method, the models between users' perception image, suit design features and users' preference are built.

The interrelationship between the three is identified by establishing PLS models between users' perception image, suit design features and users' preference. According to the coefficients of the models, the optimization schemes of men's suits considering users' perception image and preference are proposed. Verification results show that the optimization schemes are significantly better than other schemes.

The results of this paper can be used for consumer demand-oriented clothing design and provide references and methods for converting consumer's perceived needs into clothing design features.

It is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.

To address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.

The experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.

The issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.

This study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.