Ching-I Cheng

An intelligent clothes search system based on fashion styles

This work presents an intelligent clothes search system based on domain knowledge, targeted at creating a virtual assistant to search clothes matched to fashion and userpsila expectation using all what have already been in real closet. All what garment essentials and fashion knowledge are from visual images. Users can simply submit the desired image keywords, such as elegant, sporty, casual, and so on, and occasion type, such as formal meeting, outdoor dating, and so on, to the system. And then the fashion style recognition module is activated to search the desired clothes within the personal garment database. Category learning with supervised neural networking is applied to cluster garments into different impression groups. The input stimuli of the neural network are three sensations, warmness, loudness, and softness, which are transformed from the physical garment essentials like major color tone, print type, and fabric material. The system aims to provide such an intelligent user-centric services system functions as a personal fashion advisor.

A Decision Making Framework for Dressing Consultant

The project, Dressing Consultant, aims to provide a system which functions as a personal wearing advisor to help general users choose a correct clothing for occasions. ALCOVE (attention learning covering network) neural network model is used to train the matchmaker as a fashion editor. In addition, image processing techniques are employed at pre-processing stage to obtain the essential data of garments and to build a digital wardrobe for individuals. On the occasions when user has trouble finding an outfit for a special event, what user could do is to make a decision of the style of apparel to the system and let the system go through piece of garments in the digital wardrobe, and the matchmaker will then find several matched pairs. Eventually, the most similarly suitable and matched garments pair is shown in 3D show room. This paper focuses on making decision of correct clothing according to those classifying and matching rules extracted from fashion industry

Discovering Dressing Knowledge for an Intelligent Dressing Advising System

Our research aims to develop a system to help women choose correct attire for attending a specific occasion using all of what have already been in their closets. Many different computer theories and techniques are gathered in the project. Category learning with supervised neural networking is applied to cluster garments into different impression groups. Fuzzy theories are applied for gathering fashion match rules. In addition, modeling and virtual dressing techniques are used for representing matched garments pair in digital show room. User can simply submit her queries to the system on the occasions when the user has trouble finding an outfit for a special event. After enquiries are received, the core is following fuzzy logic rules to search good matches in the garment database and showing the matched results in the show room. This paper focuses on how garment classification and matching rules retrieved from fashion stylists.

A digital tutor for learning fashion design

Computer-aided instruction (CAI) is an interactive instructional technique that effectively improves the students’ understandings. However, CAI tool is currently lack of use in teaching and learning fashion design. The present research hence proposes a tool functions as a digital tutor poviding an intuitive visual aid for learning principles. The interfaces are designed based on two viewpoints: fashion style and design elements. Style Cognition Space and Style Cluster View are designed for visualizing fashion style knowledge, while Comparison View of Design Elements Effects and Elementary Attribute Space are designed for visualizing knowledge of design elements. Several skills of graphic user interface and information visualization are used, such as Details on demand, Portals, and Zoom. To obtain evaluations from users, three workshops of fashion design were held. Using questionnaires, all participants were asked to answer several questions about ease of use, effectiveness, and satisfaction. Whether professional lecturers or inexperienced novices at fashion, all users rate the system as highly satisfactory after use.

Two-dimensional non-photorealistic drawings on mobile devices

This paper presents fast approaches to simulate four different 2D non-photorealistic drawing styles (i.e., Daub, Printmaking, Line Drawing and Embossing) on mobile phones, through exploiting several image processing techniques. Taking into careful consideration of the limited hardware support and capabilities of mobile phones, we keep the drawing methods of low complexity. To reduce computation, we consider only important features that best represent each drawing style, and select the corresponding image processing techniques that can achieve the desired effect.

Incorporating Semantics into an Intelligent Clothes Search System Using Ontology

The research aims to develop a novel ontology structure for incorporating semantics into an intelligent clothes search system. It helps users to choose appropriate attire fit to the desired impression for attending a specific occasion using all of what is already in their closets. The ontology structure proposed in the paper takes a single piece of garment as the most specific instance. Hence, pictures of user’s garment items are firstly fed. An internal image processing module is then invoked to extract attributes from each piece of garment through a series of image processing techniques, such as color distribution. Finally, the derived information is attached to the ontology. In runtime, the system follows the requirements, such as mood keyword, occasion type, and weather status, to search for matched outfits with the help of semantic searching of the ontology.

Sensation Based Clothes Search System

The research aims to develop an automatic sensational clothes search system. It is an application of visual-based knowledge extraction and cognitive science, helping women choose a garment which matches the desired visual impression using all what have already been in their closets. All garment essentials and fashion knowledge are from visual images. Users simply submit the desired image keywords, such as elegant, sporty, casual, and occasion type, such as formal meeting, outdoor dating, to the system. Then sensational cognition model is activated to search the desired clothes within the personal garment database. Category learning with supervised neural networking is applied to cluster garments into different impression groups. The input stimuli of the neural network are three sensations, warmness, loudness, and softness, which are transformed from the physical garment essentials like major color tone, print type, and fabric material. This paper focuses on the transformation between physical essentials and cognitive image.