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• A novel fashion product recommendation method using multimodal information. • Integrating both text and image mining techniques. • Facilitating similar and mix-and-match fashion product recommendations. • Leveraging text-based product attributes and image features in the recommendations. Fashion recommendation has attracted much attention given its ready applications to e-commerce. Traditional methods usually recommend clothing products to users on the basis of their textual descriptions. Product images, although covering a large resource of information, are often ignored in the recommendation processes. In this study, we propose a novel fashion product recommendation method based on both text and image mining techniques. Our model facilitates two kinds of fashion recommendation, namely, similar product and mix-and-match, by leveraging text-based product attributes and image features. To suggest similar products, we construct a new similarity measure to compare the image colour and texture descriptors. For mix-and-match recommendation, we firstly adopt convolutional neural network (CNN) to classify fine-grained clothing categories and fine-grained clothing attributes from product images. Algorithm is developed to make mix-and-match recommendations by integrating the image extracted categories and attributes information are with text-based product attributes. Our comprehensive experimental work on a real-life online dataset has demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

‘ The word “fashion” is synonymous with the word “change”. Fashion begins with fabrics and fabrics begin with colour’ . This famous remark/definition of ‘fashion’ must now be revised in the era of digital technology. In this paper, we propose a novel print design concept, from 3D garments to 2D textiles. By taking advantage of the cutting-edge developments in surface parameterisation, cloth simulation and texture assignment, we develop a computer system that allows designers to create figure-flattering prints directly onto 3D garments, and will output 2D pattern pieces with matched texture that are ready for digital printing and garment production. It reverses the traditional design process from 2D fabrics to 3D garments. The results produced by the proposed method guarantee textural continuity in both garment and pattern pieces. It not only releases apparel makers from the tedious work of matching texture along seams, but also provides users with a new tool to create one-of-a-kind fashion products by designing personalised prints. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, by the integration of design methodology theories with evolutionary computation, a new design system is developed to evolve preferred designs on complex marbling patterns using interactive ‘perceptual selection’. The system is formulated in a way to assist the productive–deductive–inductive design reasoning process of the users. Therefore, complex mathematical functions do not cognitively overload the designers, who are released for more critical tasks of aesthetic assessment and new design rules induction. With the implementation on a graphics-processing unit (GPU), real-time complex marbling patterns can be created by the system. The system encourages creativity in the design process and accelerates new design generation. In addition, the resulting patterns fulfil the textile industry requirements of repeat and can be output as vector images. [Copyright &y& Elsevier]

Abstract: In this paper, a customised fashion design system is proposed for non-professional users (general customers) to create their preferred fashion designs in a user-friendly way. The proposed sketch design system consists of a sketch representation and composing method, an interactive genetic algorithm (IGA)-based design model, and a user-friendly interface. The sketch representation and composing method generates feasible design sketches, based on the design parameters defined by the IGA-based design model, and the sketches are presented to customers via the user-friendly interface. Experimental results have demonstrated that the proposed system is effective in generating fashion design sketches reflecting users’ preference. [Copyright &y& Elsevier]

Abstract: Human body modeling is a central task in computer graphics. In this paper, we propose an intelligent model customization method, in which customer’s detailed geometric characteristics can be reconstructed using limited size features extracted from the customer’s orthogonal-view photos. To realize model customization, we first propose a comprehensive shape representation to describe the geometrical shape characteristics of a human body. The shape representation has a layered structure and corresponds to important feature curves that define clothing size. Next, we identify and model a novel relationship model between 2D size features and 3D shape features for each cross-section using real subject scanned data. We predict a customer’s cross-sectional 3D shape based on size features extracted from the customer’s photos, and then we reconstruct the customer’s shape representation using predicted cross-sections. We develop a new deformation algorithm that deforms a template model into a customized shape using the reconstructed 3D shape representation. A total of 30 subjects, male and female, with varied body shapes have been recruited to verify the model customization method. The customized models show high degree of resemblance of the subjects, with accurate body sizes; the accuracy of the models is comparable to scan. It shows that the method is a feasible and efficient solution for human model customization that fulfills the specific needs of the clothing industry. [Copyright &y& Elsevier]

Abstract: Identifying the optimal cluster number and generating reliable clustering results are necessary but challenging tasks in cluster analysis. The effectiveness of clustering analysis relies not only on the assumption of cluster number but also on the clustering algorithm employed. This paper proposes a new clustering analysis method that identifies the desired cluster number and produces, at the same time, reliable clustering solutions. It first obtains many clustering results from a specific algorithm, such as Fuzzy C-Means (FCM), and then integrates these different results as a judgement matrix. An iterative graph-partitioning process is implemented to identify the desired cluster number and the final result. The proposed method is a robust approach as it is demonstrated its effectiveness in clustering 2D data sets and multi-dimensional real-world data sets of different shapes. The method is compared with cluster validity analysis and other methods such as spectral clustering and cluster ensemble methods. The method is also shown efficient in mesh segmentation applications. The proposed method is also adaptive because it not only works with the FCM algorithm but also other clustering methods like the k-means algorithm. [Copyright &y& Elsevier]

Abstract: The traditional apparel product development process is a typical iterative ‘optimization’ process that involves trial-and-error. In order to confirm the design and achieve a satisfactory fit, a number of repeated cycles of sample preparation, trial fitting and pattern alteration must be conducted. The process itself is time-consuming, costly, and dependent on the designer’s skills and experience. In this paper, a novel computer aided design (CAD) solution for virtual try-on, fitting evaluation and style editing is proposed to speed up the clothing design process. A series of new techniques from cross parameterization, geometrical and physical integrated deformation, to novel editing methods are proposed. First, a cross parameterization technique is employed to map clothing pattern pieces on a model surface. The pattern can be precisely positioned to form the initial shape with low distortion. Next, a new deformation method called hybrid pop-up is proposed to approximate the virtual try-on shape. This method is an integration of geometrical reconstruction and physical based simulation. In addition, user interactive operations are introduced for style editing and pattern alteration in both 2D and 3D manners. The standard rules regulating pattern editing in the fashion industry can be incorporated in the system, so that the resulting clothing patterns are suitable for everyday production. [Copyright &y& Elsevier]

Abstract: It is generally accepted that there is a relationship between body dimensions, body movement and clothing wearing ease design, and yet previous research in this area has been neither sufficient nor systematic. This paper proposes a method to measure the human body in the static state and in 17 dynamic postures, so as to understand dimensional changes of different body parts during dynamic movements. Experimental work is carried out to collect 30 measurements of 10 male Chinese subjects in both static and dynamic states. Factor analysis is used to analyse body measurement data in a static state, and such key measurements describe the characteristics of different body figures. Moreover, one-way ANOVA is used to analyse how dynamic postures affect these key body measurements. Finally, an application of the research results is suggested: A dynamic block patternmaking method for high-performance clothing design. [Copyright &y& Elsevier]

Abstract: Clothing computer design systems include three integrated parts: garment pattern design in 2D/3D, virtual try-on and realistic clothing simulation. Some important results have been obtained in pattern design and clothing simulation since the 1980s. However, in the area of virtual try-on, only limited methods have been proposed which are applicable to some defined garment styles or under restrictive sewing assumptions. This paper presents a series of new techniques from virtually sewing up complex garment patterns on human models to visualizing design effects through physical-based real-time simulation. We first employ an hierarchy of ellipsoids to approximate human models in which the bounding ellipsoids are optimized recursively. We also present a new scheme for including contact friction and resolving collisions. Four types of user interactive operation are introduced to manipulate cloth patterns for pre-positioning, virtual sewing and later obtaining cloth simulation. In the cloth simulation, we propose a simplified cloth dynamic model and an integration scheme to realize a high quality real-time cloth simulation. We demonstrate the robustness of our proposed systems by complex garment style virtual try-on and cloth simulation. [Copyright &y& Elsevier]

In this paper, production control of an actual industrial semiconductor packaging assembly line is studied. An evolutionary optimization methodology is developed to synthesize decision support systems for providing both crisp-logic and fuzzy-logic production control for the assembly line. The performance of such decision support systems is compared with that of the system originally implemented by the manufacturer. It is thus demonstrated that the proposed evolutionary methodology is effective in synthesizing decision support systems for both crisp-logic and fuzzy-logic control of the semiconductor packaging assembly production. It indicates that the proposed methodology can also synthesize decision support systems for productivity improvement in other complex manufacturing environment. [Copyright &y& Elsevier]

Multi-scale feature fusion is a commonly-used module in existing deep-learning models, and feature misalignment occurs in the process of feature fusion. The spatial misalignment hinders the learning of semantic representation with multi-scale levels, but which has not received much attention. This misalignment problem is caused by the feature position shift after using the convolution and interpolation operation in feature fusion. To solve the misalignment problem, this paper formulates the shift error mathematically and proposes a plug-and-play unbiased feature position alignment strategy to align convolution with interpolation. As a model-agnostic approach, unbiased feature position alignment can boost the performance of different models without introducing extra parameters. Furthermore, the unbiased feature position alignment is applied to build an unbiased human pose estimation method. Experimental results have demonstrated the effectiveness of the proposed unbiased pose model in comparison to the state-of-the-arts, especially in the low-resolution field. The codes are shared at https://github.com/WangChen100/Unbiased-Feature-Position-Alignment-for-Human-Pose-Estimation. [ABSTRACT FROM AUTHOR]

Abstract: Packing problems are combinatorial optimization problems that concern the allocation of multiple objects in a large containment region without overlap and exist almost everywhere in real world. Irregular objects packing problems are more complex than regular ones. In this study, a methodology that hybridizes a two-stage packing approach based on grid approximation with an integer representation based genetic algorithm (GA) is proposed to obtain an efficient allocation of irregular objects in a stock sheet of infinite length and fixed width without overlap. The effectiveness of the proposed methodology is validated by the experiments in the apparel industry, and the results demonstrate that the proposed method outperforms the commonly used bottom-left (BL) placement strategy in combination with random search (RS). [Copyright &y& Elsevier]

Abstract: This paper reports the development of a unique model to quantify the environmental impact made by various textile fibres and also to position them in terms of ecological sustainability. A wide range of textile fibres and the major contributing factors in terms of environmental impact during manufacturing phase (starting from growth/extraction stage to production of a useful fibre which can be spun) have been selected. Amount of oxygen produced/carbon-di-oxide absorbed consequently contributing to off-set global warming during the production phase of a fibre, utilisation of renewable resources, land use, usage of fertilisers and pesticides, fibre recyclability and biodegradability of chosen fibres have been considered. Amount of energy consumed, quantity of water utilised and amount of green house gases emitted have been considered for life cycle inventory (LCI) and a life cycle impact assessment (LCIA) study has been accomplished to derive certain impact categories pertaining to the damage created to human health, ecosystem quality and resources, which will elucidate the characteristics of ecological sustainability. A scoring system based on the above mentioned factors which predominantly determine ecological sustainability has been framed, from which an Environmental Impact index (EI) has been developed. Further, an Ecological Sustainability Index (ESI) has been derived from EI values for the chosen fibres. According to this system, organic cotton is the most and acrylic is the least preferred fibre. A sensitivity study has been done to check the robustness of the developed model and the results of the same have been reported. [Copyright &y& Elsevier]

Abstract: Carbon footprint has become a term often used by the media in recent days. The human carbon footprint is professed to be a very serious global threat and every nation is looking at the possible options to reduce it since its consequences are alarming. A carbon footprint is a measure of the impact of human activities on earth and in particular on the environment; more specifically it relates to climate change and to the total amount of greenhouse gases produced, measured in units of carbon dioxide emitted. Effort of individuals in minimizing the carbon footprint is vital to save our planet. This article reports a study of the carbon footprint of various types of shopping bags (plastic, paper, non-woven and woven) using life cycle impact assessment (LCIA) technique in two stages. The first stage (baseline study), comprised the study of the impact of different types of shopping bags in the manufacturing phase, without considering their usage and disposal phases (cradle to gate stage). The LCIA was accomplished by the IPCC 2007 method, developed by the Inter Panel on Climate Change in SIMAPRO 7.2. The GWP (Global Warming Potential) values calculated by the IPCC 2007 method for 100 years were considered as a directive to compare the carbon footprint made by the different types of shopping bags under consideration. The next stage was the study of the carbon footprint of these bags including their usage and disposal phases (cradle to grave stage) and the results derived were compared with the results derived from the baseline study, which is the major focus of this research work. The values for usage and end-of-life phases were obtained from the survey questionnaire performed amongst different user groups of shopping bags in China, Hong Kong and India. The results show that the impact of different types of shopping bags in terms of their carbon footprint potential is very high if no usage and disposal options were provided. When the carbon footprint values from different disposal options were compared, the carbon footprint values were lower in the case where a higher percentage of reuse is preferred to recycling and disposing to landfill. The results indicate that a higher percentage of reuse could significantly scale down the carbon footprint. Once the shopping bags reached the point where they can no longer be reused, they must be forwarded to recycling options, rather than being disposed to landfill. At this juncture, consumer’s perceptions and behaviours in connection with the respective government’s policies in promoting & facilitating recycling systems could be critical in reducing the carbon footprint of various shopping bags. [Copyright &y& Elsevier]

Abstract: In today’s fashion retailing business, providing “fashion mix-and-match” or “fashion coordination” recommendations is a ‘must’ strategy to enhance customer service and improve sales. In this study, a fashion mix-and-match expert system is developed to provide customers with professional and systematic mix-and-match recommendations automatically. The system can capture the knowledge and emulate the decisions of fashion designers on apparel coordination and its knowledge base can store the literal form of information. A set of attributes of the apparel for coordination are identified and formulated; their corresponding importance is also defined with designers’ opinions using ordered weighted averaging operators. The Fashion Coordination Satisfaction Index is devised and computed using the fuzzy screening approach to represent the satisfaction degree of the coordinating pairs of apparel product items. The experimental results demonstrate that the proposed system can generate effective mix-and-match recommendations and is now integrated with a smart dressing system used effectively in a fashion chain store company in Hong Kong. [Copyright &y& Elsevier]

In apparel manufacturing, experience and subjective assessment of production planners are used quite often to plan the production schedules in their fabric-cutting departments. The quantities of cut-pieces produced by fabric-cutting departments based on these non-systematic schedules cannot fulfil the cut-piece requirements of the downstream sewing lines and minimize the makespan. This paper proposes a genetic algorithms (GAs) approach to optimize both the cut-piece requirements and the makespan of the conventional fabric-cutting departments using manual spreading and cutting methods. An optimization model for the manual fabric cutting process based on GAs was developed. Two sets of production data were collected to validate the performance of the model and the experimental results were obtained. From the results, it can be found that both the makespan and cut-piece fulfilment rates are improved in which the latter is improved significantly. [ABSTRACT FROM AUTHOR]