Purpose




With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.




Design/methodology/approach




This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.




Findings




This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.




Originality/value




Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

/pdf/apparel-recommendation-system-evolution-an-empirical-review-31x9bt2zk3.pdf

Apparel recommendation system evolution: an empirical review

3D parametric human face modeling for personalized product design

Statistical learning of human body shape can be used for reconstructing or estimating body shapes from incomplete data, semantic parametric design, modifying images and videos, or simulation. A digital human body is normally represented in a high-dimensional space, and the number of vertices in a mesh is far larger than the number of human bodies in public available databases, which results in a model learned by Principle Component Analysis (PCA) can hardly reflect the true variety in human body shapes. While deep learning has been most successful on data with an underlying Euclidean or grid-like structure, the geometric nature of human body is non-Euclidean, it will be very challenging to perform deep learning techniques directly on such non-Euclidean domain. This paper presents a deep neural network (DNN) based hierarchical method for statistical learning of human body by using feature wireframe as one of the layers to separate the whole problem into smaller and more solvable sub-problems. The feature wireframe is a collection of feature curves which are semantically defined on the mesh of human body, and it is consistent to all human bodies. A set of patches can then be generated by clustering the whole mesh surface to separated ones that interpolate the feature wireframe. Since the surface is separated into patches, PCA only needs to be conducted on each patch but not on the whole surface. The spatial relationships between the semantic parameter, the wireframe and the patches are learned by DNN and linear regression respectively. An application of semantic parametric design is used to demonstrate the capability of the method, where the semantic parameters are linked to the feature wireframe instead of the mesh directly. Under this hierarchy, the feature wireframe acts like an agent between semantic parameters and the mesh, and also contains semantic meaning by itself. The proposed method of learning human body statistically with the help of feature wireframe is scalable and has a better quality.

/pdf/parametric-design-for-human-body-modeling-by-wireframe-4xpy60d8au.pdf

Parametric design for human body modeling by wireframe-assisted deep learning

The construction industry is currently driving to reach a high level of achievement for future sustainable built environments. The UK and USA models for the building design and construction process have been developed to enable relevant stakeholders to work closely from the beginning of a project. Indeed, design production is particularly significant, particularly at the concept design stage, as design decisions cost less to change and there is greater opportunity for change at the concept design stage. These are important considerations for this study. In addition, collaboration can significantly reduce redundant loops of performance in the subsequent processes leading to savings both in cost and time. However, the traditional design phases in the USA and Thailand have faced barriers due to silos of effort, a lack of overall project awareness, inefficient collaboration between silos, and poor communication among silos due to the fragmentation of the functional disciplines involved.
Furthermore, stakeholders working in partnership in the AEC industry are connected together in a complex way so information sharing is not straightforward; however, they are now supported by digital tools and communication technology, which can mediate this collaborative process. Effective collaboration can be promoted by technology through existing digital tools, for example, CAD or BIM. These tools can enable important increases in productivity through the strategic sharing of information. However, these are not appropriate tools for the concept design stage due to the complex information involved and the difficult user interfaces. This difficult interface leads to consumption of memory and an obstruction of the mental workflow, which does not support designers at the concept design stage. Moreover, the purpose of collaboration among multidisciplinary and multi-functional teams is to enable the integration of other disciplines, to maintain and sustain knowledge and information sharing, and to predict colleague behaviour. Knowledge sources and the retrieval of knowledge and information are significant; however, communication has pros and cons, and current tools, such as email or instant messaging, are difficult to use for the purpose of collaboration.
Therefore, this research has developed an innovative digital tool to facilitate collaboration and support designers at the concept design stage. This tool enhances the creative thinking process by emphasising the streaming of the cognitive workload. In addition, collaborative communication supports the creation and structure of contents, posts and replies and supports designers to retrieve information. Collaborators/stakeholders can therefore share space and information. The research methodology for this study was designed to collect data in Thailand; it included the design and development of a prototype, and an evaluation of a case study in Thailand. The prototype is the first pilot of the digital tool to support designers at the concept design stage and could be further developed for use in practice.

A digital prototype for collaborative conceptual design in the Thai AEC sector

Fitting elliptical arcs to strokes of an input sketch is discussed We describe an approach which automatically combines existing algorithms to get a balance of speed and precision For measuring precision, we introduce fast metrics which are based on perceptual criteria and are tolerant of sketching imperfections We return a likelihood estimate based on these metrics rather than deterministic yes/no result, in order that the approach can be used in higher-level collaborative-decision recognition flows

/pdf/a-fast-approach-for-perceptually-based-fitting-strokes-into-45q7p1wcfg.pdf

A fast approach for perceptually-based fitting strokes into elliptical arcs

This paper describes a new method for recognizing overtraced strokes to 2D geometric primitives, which are further interpreted as 2D line drawings. This method can support rapid grouping and fitting of overtraced polylines or conic curves based on the classified characteristics of each stroke during its preprocessing stage. The orientation and its endpoints of a classified stroke are used in the stroke grouping process. The grouped strokes are then fitted with 2D geometry. This method can deal with overtraced sketch strokes in both solid and dash linestyles, fit grouped polylines as a whole polyline and simply fit conic strokes without computing the direction of a stroke. It avoids losing joint information due to segmentation of a polyline into line-segments. The proposed method has been tested with our freehand sketch recognition system (FSR), which is robust and easier to use by removing some limitations embedded with most existing sketching systems which only accept non-overtraced stroke drawing. The test results showed that the proposed method can support freehand sketching based conceptual design with no limitations on drawing sequence, directions and overtraced cases while achieving a satisfactory interpretation rate.

https://link.springer.com/content/pdf/10.1007%2Fs00371-013-0844-y.pdf

New grouping and fitting methods for interactive overtraced sketches

Three-dimensional (3D) body scanning technology opens opportunities for virtual try-on and automatic made-to-measure apparel design. This paper proposes a new feature-based parametric method for modeling human body shape from scanned point clouds of a 3D body scanner [TC]2. The human body model consists of two layers: the skeleton and the cross sections of each body part. First, a simple skeleton model from the body scanner [TC]2 system has been improved by adding and adjusting the position of joints in order to better address some fit issues related to body shape changes such as spinal bending. Second, an automatic approach to extracting semantic features for cross sections has been developed based on the body hierarchy. For each cross section, it is described by a set of key points which can be fit with a closed cardinal spline. According to the point distribution in point clouds, an extraction method of key points on cross sections has been studied and developed. Third, this paper presents an interpolation approach to fitting the key points on a cross section to a cardinal spline, in which different tension parameters are tested and optimized to represent simple deformations of body shape. Finally, a connection approach of body parts is proposed by sharing a boundary curve. The proposed method has been tested with the developed virtual human model (VHM) system which is robust and easier to use. The model can also be imported in a CAD environment for other applications.

/pdf/feature-based-human-model-for-digital-apparel-design-3nfwp65sm7.pdf

Feature-Based Human Model for Digital Apparel Design

A novel method is presented based on fuzzy hybrid-based features to classify strokes into 2D line drawings, and a human computer interactive system is developed for assisting designers in conceptual design stage. Fuzzy classifiers are built based on some geometric features and speed features. The prototype system can support rapid classification based on fuzzy classifiers, and the classified stroke is then fitted with a 2D geometry primitive which could be a line segment, polyline, circle, circular arc, ellipse, elliptical arc, hyperbola, and parabola. The human computer interaction can determine the ambiguous results and then revise the misrecognitions. The test results showed that the proposed method can support online freehand sketching based on conceptual design with no limitation on drawing sequence and direction while achieving a satisfactory interpretation rate.

Using Fuzzy Hybrid Features to Classify Strokes in Interactive Sketches

Endpoint fusing plays a crucial role in the process of sketch regularization and corrects the gestures from freehand-sketched strokes, which have the imprecise connections at the endpoints and inflection point of into some closed loops. This paper proposes a novel endpoint fusing method, based on a feature descriptor called angle distribution histogram, is specialized for online freehand-sketched polyhedrons. The method comprises four main phases. Firstly, parallelism correction is used to revise and recognize user’s inputting strokes according to the linearity distribution of gestures in the angle histogram. Then, the adaptive tolerance circles with variable coefficients are applied to determine which the endpoints should be joined together. Nextly settles the order of connecting endpoints in accordance with the coordinate system weight criteria. Finally, the session of endpoints connection is carried out and ends with a clean, crisp line drawing possessing perfect connections. This approach has been tested with various examples and compared with the other existing methods in an interactive prototype system of freehand sketching recognition-endpoint fusion to evaluate the regularization quality. The experimental results show that the proposed approach overcomes these limitations embedded within the existing sketching systems, in which only simply connect the endpoints without considering the contextual constraints in the sketch. The method can perfectly support the conceptual design based on online freehand-sketched polyhedrons and provide a foundation for the further 3D reconstruction of freehand-sketched polyhedrons.

Endpoint fusing method of online freehand-sketched polyhedrons

The invention provides a stroke segmentation method based on a mixing feature for intelligent free-hand input. The method comprises the steps that sampling points of effective input strokes are extracted; speed segmentation points of the effective input strokes are obtained by the adoption of a stroke segmentation method based on a speed feature, and polygonal approximation is performed on the effective input strokes to obtain a salient point sequence; primary feature mapping is performed on the salient point sequence and a speed segmentation point sequence to obtain a speed salient point sequence; the effective input strokes are segmented to obtain a geometric segmentation point sequence; secondary mapping is performed on the speed salient point sequence and the geometric segmentation point sequence to obtain an early-stage segmentation point sequence; early-stage segmentation points are judged to obtain late-stage segmentation points. According to the stroke segmentation method, the speed feature and the geometric feature are combined to form the new mixing feature after mapping; firstly, early-stage segmentation is performed, afterwards, the single line element recognition is performed, and finally late-stage segmentation is performed. In this way, the defects of simple geometric feature segmentation and the defects of simple speed feature segmentation are effectively avoided.

Shuxia Wang

Papers

New grouping and fitting methods for interactive overtraced sketches

Feature-Based Human Model for Digital Apparel Design

Using Fuzzy Hybrid Features to Classify Strokes in Interactive Sketches

Endpoint fusing method of online freehand-sketched polyhedrons

Stroke segmentation method based on mixing feature for intelligent free-hand input