• Journal of Digital Convergence
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• v.20 no.3
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• pp.407-413
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• 2022

Existing clothing recommendation systems remain at the level of showing appropriate photos when a user selects a type of clothing he or she likes after entering his or her own body size or body size. When a user purchases clothing using such recommendation systems, there are many cases in which it does not fit or does not fit the user's body size. In this study, to solve these problems of existing clothing recommendation systems, a system was implemented in which the user receives not only size but also skin tone and recommends clothing suitable for the user's body size as well as skin tone. In this system, clothing size information obtained through web crawling was periodically stored in a database for eight male tops to recommend clothing, and the entire pixel of the clothing image was analyzed to extract color text values. In order to confirm the performance of this system, a survey was conducted on 100 male college students, and the satisfaction level was 70%. Most of the reasons for not being satisfied are that the recommended clothing is limited, so it is judged that it is necessary to expand the target clothing in the future.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Korean Institute of Interior Design Journal
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• no.28
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• pp.100-108
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• 2001

The purpose of this study is to develop recommendation of sign system and color codes in making the public interior environment more accessible for the visually impaired persons. The appropriate guidelines in designing the sign system and the color codes in the public interior environment can increase the accessibility, the mobility and the safety for the visually impaired persons including the increasing elderly. By selecting the effective sign system much more desirable results can be brought: Improvement of accessibility, mobility, increase of safety, work efficiency and the psychological stability. Sign system and the color is more important for the weak-sighted people than the normal-sighted in way finding and the orientation strategies. 53 visually impaired persons participated for this study, the answers of the questionaires by the weak-sighted persons are focused on in this research. The data, the needs of users is analysed on the basis of users-oriented. The result of this study can be considered as a basic direction of sign system design and color codes in our codes in our public environment for all people especially visually impaired persons.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.23-30
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• 2010

It is important for the strategy of cosmetic sales to investigate the sensibility and the preference degree in the environment that the makeup style has been changed focusing on the consumer center. We proposed the human sensibility ergonomics makeup recommendation system (MakeupRS) using the context sensor information applying the collaborative filtering technique as one of methods in the makeup style development centered on the consumer's sensibility and the preference. In the collaborative filtering technique, the Pearson correlation coefficient applying to the case amplification is used to calculate similarity weights between the users. To investigate the sensibility according to the effect of makeup styles, the makeup styles were analyzed in terms of 6 style factors, such as, the foundation, the color lens, the eye shadow, the eye lash, the cheek brusher, and the lipstick. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the human sensibility ergonomics makeup recommendation system.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.7
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• pp.736-747
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• 2011

This study investigates the current status and dyeing properties of various natural indigo powder dyes in the domestic market. Products from India, China, Europe are sold in the market and only a few manufacturers provide recommendation for the method of dyeing and information on the additives. Through the market research and the preliminary investigation on 21 products, 11 were selected for the dyeing experiment which include 3 Indian, 3 Chinese, 2 German, and 1 Pakistani origin indigo reduced powders, and 2 Indian origin dried indigo leaf powder. The two dyeing methods used were the precipitation method and the fresh juice method, both at $10^{\circ}C$, $25^{\circ}C$, and $60^{\circ}C$. Color difference, K/S value, and colorfastness of dyed cotton fabrics were examined. Indian reduced indigo powder showed the highest K/S value, deep dyeing, and the best color fastness. Chinese reduced indigo powder resulted in a more greenish and bluish color. Powders of dried indigo leaves were easy to use but resulted in a pale color due to low dye uptake.

• Journal of the Korean Home Economics Association
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• v.36 no.6
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• pp.57-70
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• 1998

For developing girls' brassiere, the survey on the actual wearing condition of brassiere for girls aged 9-15 if fulfilled. The findings of this study can be summarized as follows. 1) The starting age of the breast growth differs according to regions, schools, grades and body shape. Reason wearing brassiere is to sustain body shape. present brassiere type preferred most is tank-top. Motive wearing brassiere is based on the other's recommendation. Purchasing brassiere is performed largely by mother. 2) Few complaints appeared in the aspects of sewing, hook, loop, wire, wearing sense, touching sense, sweat absorption etc. Evaluation for the brassiere color and design is totally satisfied. When purchasing brassiere, beauty and color are important to all of girls. Yes or No of satisfaction for the wearing sense and comfort of brassiere is answered negatively. 3) Elementary school pupils preferred sport type, While middle school students showed an order as follows-wire

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.643-645
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• 2022

많은 온라인 쇼핑몰에서 색상 기반 필터링 서비스나 추천 시스템을 제공하지만, 수동 분류는 많은 시간이 들고 오류 위험이 있다. 본 연구의 실험에서는 먼저 분석할 의류 이미지를 실루엣 분석으로 수행한 경우와 수행하지 않는 경우의 k-평균 군집화 알고리즘으로 가장 우세한 색상 군집의 중심값을 도출하는데, 만약 군집 개수가 2개 이상이면 보다 큰 군집의 중심값만을 고려한다. 이 중심값을 이용해 사전 학습한 k-최근접 이웃 알고리즘으로 색상 클래스를 분류한다. 실험 결과 실루엣 분석을 수행하지 않은 k-평균 군집화 알고리즘을 사용한 분류 방식이 정확도와 수행 시간 모두 매우 준수하였으나, 배경색이 존재하여 의류 색 분석에 영향을 줄 수 있는 경우 잘못 분류한다는 문제도 있다.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.461-463
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• 2023

MZ세대를 중심으로 자기관리를 열심히 하는 사람들이 증가함에 따라 화장의 기본이 되는 개인 피부톤(퍼스널 컬러)을 찾는 것이 중요시되고 있다. 현재 대다수 사람은 자신에게 어울리는 퍼스널 컬러를 찾기 위해 높은 비용을 지불하여 전문가를 이용하거나 객관적이고 정량화된 기준 없이 오랜 시간을 투자하여 스스로 퍼스널 컬러를 찾는 등 시간과 비용 측면에서의 한계점을 가지고 있다. 본 논문에서는 이를 보완하기 위해 이미지 기반 인공지능 기술(객체 탐지, 객체 분할, BeautyGAN)을 적용하여 데이터 기반의 정량적인 기준을 생성하고, 퍼스널 컬러에 알맞은 화장품 추천 웹 서비스를 제안한다.