• 한국의류학회지
• /
• 제44권2호
• /
• pp.354-368
• /
• 2020

Accurate and versatile image data-sets are essential for fashion AI research and AI-based fashion businesses based on a systematic attribute classification system. This study constructs a color and texture attribute hierarchical classification system by collecting fashion item images and analyzing the metadata of fashion items described by consumers. Essential dimensions to explain color and texture attributes were extracted; in addition, attribute values for each dimension were constructed based on metadata and previous studies. This hierarchical classification system satisfies consistency, exclusiveness, inclusiveness, and flexibility. The image tagging to confirm the usefulness of the proposed classification system indicated that the contents of attributes of the same image differ depending on the annotator that require a clear standard for distinguishing differences between the properties. This classification system will improve the reliability of the training data for machine learning, by providing standardized criteria for tasks such as tagging and annotating of fashion items.

• 한국의류학회지
• /
• 제45권6호
• /
• pp.949-964
• /
• 2021

Accumulation of high-quality data is crucial for AI learning. The goal of using AI in fashion service is to propose of a creative, personalized solution that is close to the know-how of a human operator. These customized solutions require an understanding of fashion products and emotions. Therefore, it is necessary to accumulate data on the attributes of fashion products and fashion emotion. The first step for accumulating fashion data is to standardize the attribute with coherent system. The purpose of this study is to propose a fashion emotional classification system. For this, images of fashion products were collected, and metadata was obtained by allowing consumers to describe their emotions about fashion images freely. An emotional classification system with a hierarchical structure, was then constructed by performing frequency and CONCOR analyses on metadata. A final classification system was proposed by supplementing attribute values with reference to findings from previous studies and SNS data.

• 한국의류산업학회지
• /
• 제8권6호
• /
• pp.647-654
• /
• 2006

The purpose of this study is to identify the differences among goods attribute and brand awareness on fashion brand type. we were intended to suggest characteristics of each consumer group by identifying the differences of consumers' purchasing activities. 672 of consumers by brand who frequently purchase casual brand were chosen for the analysis according to common brand classification of national brand, private brand and no brand. For the purpose of data analysis, we performed factorial analysis of measuring tools and credibility test. Concerning the differences of goods attribute, brand awareness by brand type, MANOVA, ANOVA was employed, complimented with Sheffe-test as a post hoc test in case of occurrence of any differences by group. The findings from the analysis are described in the following. Regarding goods attribute by fashion brand type, there existed a significant difference between brand types in all the sub factors of goods attribute such as product attribute, shop attribute, and price attribute. Especially, the difference of product attribute is much more significant in the areas of material suitableness, product assortment, aesthetic expression, size & quality, clothing maintenance, and clothing comfortableness. In case of shop attribute, there was a significant difference between groups in all the factors such as shop environment, convenience of shopping, sales promotion, service quality of sales clerk, location, and shop reputation. Concerning price attribute, we found a significant difference between groups in the factors of price value, price reasonableness, price information, and economical efficiency of price. As for the difference of brand awareness by brand type, among other factors, brand value had a difference between groups; that is, private brand was found to obtain the highest brand value awareness.

• 한국의류학회지
• /
• 제35권9호
• /
• pp.1099-1111
• /
• 2011

This study identified the types of consumer review information about apparel products based on consumer satisfaction/dissatisfaction with the availability/non-availability of consumer review information for online stores. Data were collected from 318 females aged 20s' to 30s', who had significant experience in reading consumer reviews posted on online stores. Consumer satisfaction/dissatisfaction with availability or non-availability of review information on online stores is different for information in regards to apparel product attributes, product benefits, and store attributes. According to the concept of quality elements suggested by the Kano model, two types of consumer review information were determined: Must-have information (product attribute information about size, fabric, color and design of the apparel product; benefit information about washing & care and comport of the apparel product; store attribute information about responsiveness, disclosure, delivery and after service of the store) and attracting information (attribute information about price comparison; benefit information about coordination with other items, fashionability, price discounts, value for price, reaction from others, emotion experienced during transaction, symbolic features for status, health functionality, and eco-friendly feature; store attribute information about return/refund, damage compensation and reputation/credibility of online store and interactive and dynamic nature of reviews among customers). There were significant differences between the high and low involvement groups in their perceptions of consumer review information.

• 스마트미디어저널
• /
• 제13권4호
• /
• pp.57-64
• /
• 2024

패션 이미지에 포함된 소재, 색상, 핏 등의 속성은 소비자가 의류를 구매하는 데 있어서 중요한 요인이다. 그러나 의류 속성을 분류하는 과정은 많은 인력을 필요로 하고, 작업자의 주관적인 판단에 의존하기 때문에 일관성이 떨어진다. 이러한 문제를 완화하기 위해 인공지능을 활용하여 패션 이미지의 의류 속성을 분류하는 연구가 필요하다. 기존 연구에서는 주로 상의 또는 하의 중 하나의 항목에 대한 의류 속성을 분류하는 것에 초점을 두고 있기 때문에 전신 패션 이미지의 경우에는 상의와 하의의 속성을 동시에 파악할 수 없다는 한계가 있었다. 본 연구는 패션 이미지의 상의와 하의를 구분하여 각 항목의 카테고리와 의류 소재의 속성을 분류할 수 있는 딥러닝 모델을 제안한다. 본 연구에서 딥러닝 모델 ResNet과 EfficientNet를 이용하였고, 학습에 활용한 데이터셋은 패션 이미지 1,002,718장과 의류 카테고리와 소재 속성을 포함한 라벨 총 125개를 사용하였다. Weighted F1-Score를 기준으로 ResNet은 0.800, EfficientNet는 0.781로 ResNet이 더 우수한 성능을 보였다.

• 한국의류학회지
• /
• 제22권3호
• /
• pp.374-383
• /
• 1998

Apparel Quality was one of the most important elements to evaluate the reputations of companies and products which affect the consumer's purchasing behavior. From researches on apparel quality, there was no common concept of quality as well as no common dimensions. The purposes of this study were to identify apparel quality concept and to classify the multi-dimensional concept of apparel quality. The research was carried out in theoretical as well as empirical studies. The theoretical study was conducted to find out apparel quality concept and divide apparel quality concept into four dimensions groups. The empirical study followed the theoretical study to confirm the multi-dimensional concept of apparel quality. The empirical study was investigated that the questionnaire was administered to 634 housewives in Seoul, Kwangju, and Busan during the fall of 1996. The data were analysed by LISREL analysis. This study identified that apparel quality was characteristics of consumer's desires for apparel. The results of the theoretical study verified that apparel quality concept was organized into four different dimensions: physical attribute, physical function, instrumental performance, and expressive performance.