• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.676-677
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• 2022

In this paper, we propose a new method based on a deep learning model with an optimized dynamic decay learning rate and improved model structure to achieve fast and accurate classification of fashion clothing images. Experiments are performed using the model proposed in the Fashion-MNIST dataset and compared with methods of CNN, LeNet, LSTM and BiLSTM.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.10
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• pp.1412-1423
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• 2002

The purpose of this study is to objectively explain the visual sensibility of clothing torture that satisfies the consumer's sensibility. The photo stimuli on clothing texture are divided into hard, soft transparent and brilliant. For the study of image 38 kinds of costume samples is used. The Study was measured by using Semantic Differential method. The subjects were 410 females in twenties. The data were analyzed by factor analysis, ANOVA, MDS and regression analysis. Data were analyzed by SPSS. The major findings of this research were as follows: 1. As a result of the factor analysis,5 factors of visual sensibility were consist of high qualities, touches, looks, lightness, and warmness or coolness.2. There were significant difference in visual sensibility based on classification of clothing texture.3. The clothing texture was classified as thin-full, flat-lumpy. 4. As a result of the regression analysis, preferences of consumers can be connected directly with buying behavior and satisfaction can be closely related with preferences and positive buying behavior.

• Smart Media Journal
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• v.13 no.4
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• pp.57-64
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• 2024

Attributes such as material, color, and fit in fashion images are important factors for consumers to purchase clothing. However, the process of classifying clothing attributes requires a large amount of manpower and is inconsistent because it relies on the subjective judgment of human operators. To alleviate this problem, there is a need for research that utilizes artificial intelligence to classify clothing attributes in fashion images. Previous studies have mainly focused on classifying clothing attributes for either tops or bottoms, so there is a limitation that the attributes of both tops and bottoms cannot be identified simultaneously in the case of full-body fashion images. In this study, we propose a deep learning model that can distinguish between tops and bottoms in fashion images and classify the category of each item and the attributes of the clothing material. The deep learning models ResNet and EfficientNet were used in this study, and the dataset used for training was 1,002,718 fashion images and 125 labels including clothing categories and material properties. Based on the weighted F1-Score, ResNet is 0.800 and EfficientNet is 0.781, with ResNet showing better performance.

• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.95-105
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• 2024

With the recent adoption of AI by various clothing shopping platforms and related industries to meet consumer needs and enhance purchasing power, the necessity for accurate classification of clothing categories and colors has surged. This paper aims to address this issue by developing a deep learning model that classifies various clothing items and their colors within a single image using buyer review images. After directly crawling buyer review image data and performing various preprocessing steps such as data augmentation, we utilized the YOLOv10 model to detect clothing objects and classify them into categories. Subsequently, to improve color extraction, we implemented a cropping method to isolate clothing regions in the images and calculated the similarity with a color chart to extract the most similar color names. Our experimental results show that our approach is effective, with performance increasing with model size and augmentation scale. The employed model showed stable performance in both clothing category and color extraction, proving its reliability. The proposed system not only enhances customer satisfaction and purchasing power by accurately classifying clothing categories and colors based on user review images but also lays the foundation for further research in automated fashion analysis. Moreover, it possesses the scalability to be utilized in various fields of the related industry, such as fashion trend analysis, inventory management, and marketing strategy development.

• Korean Journal of Human Ecology
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• v.10 no.4
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• pp.365-378
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• 2001

This study aims to investigate the situation of clothing remains in the museums located in Jeolla Province and the problems appeared in the exhibitions, management and safekeeping in order to provide the basic data of costume studies which contribute to understanding our inherent clothing culture. The method of this study included classification of the clothing remains of the thirteen museums in Jeolla province according to the system used in the National Museum of Korea, the research data was analyzed by using charts. The result of this study is as following : The total of 8696 clothing remains were inspected, and these were consisted of 78% ornaments, 9.4% clothings, 5.4% hats, 4.4% shoes, 1.8% belts and buckles, 1.0% boxes for hats and clothes.

• Journal of the Korean Society of Clothing and Textiles
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• v.23 no.2
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• pp.250-261
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• 1999

This study is designed to classify consumer groups based on their perception toward department store credit cards and the behavior they exhibit during the purchase of clothing. This classification is based on the study of factors taken into consideration during shopping and disparities in credit cared usage., The specific goals of this study are the following : First it is to classify female consumers over age 20 into "shopping orientation" types and "clothing purchase behavior" types according to their perception towards department store credit care usage. Second it is to discover the degree of perceived utility of department store credit card in clothing purchases. Third finally it is to assist a department store credit card market researcher establish a marketing strategy to best address consumers; needs and wants in credit card purchases The study methodology utilized and the results found were that : 1. The division of consumers into positive and negative groups based on factor analysis with the positive group found to have favorable attitudes towards department store credit card usage. 2. Classification of female consumers into three " shopping orientations" : fashion purchasing economic value purchasing and convenience purchasing. The positive group were predominantly fashion convenience purchasers who valued low cost and convenience over "fashionability" 3. The three classes of "purchase behavior" used were impulse buying planned buying and unplanned buying. The positive group those who had favorable attitudes toward department store credit cards. made mostly impulse and unplanned purchases while the negative group made largely planned purchasee the negative group made largely planned purchase.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.4
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• pp.691-707
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• 2020

This study used big data analysis as informatics that identified keywords related to new-tro fashion; in addition, it conducted differences and types of classification according to demographic characteristics. First, it has been shown that two different generations, the Millennials and the older generation, coexist as important keywords in the context of new-tro fashion. Second, according to age, it has been shown that the keywords that appear in new-tro fashion are taken differently. In most regional keywords that differed in the classification, respondents in their 20s, 30s and 40s were classified as emotional, while those in their 50s or older perceived as factual phenomena. The results of eliciting keywords in new-tro fashion through big data analysis, keywords that reflect phenomena, design details and considerations, fashion styles, fashion brands, fashion items, social media, influence, and emotional adjectives. This study confirmed the meaning of new-tro fashion based on past that can give enjoyment to the new generation and memories to the older generation.

• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.6
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• pp.1027-1039
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• 1995

Clothing fitness is strongly required in the apparel industry, and draping is an effective tool to increase fitness to the wearers. A more sophisticated and systematic information of the somatotype, accordingly, is necessary for better cress form design. This study was performed to provide fundamental data on middle aged women's upper torso for dress form designers and pattern makers by classifying the somatotype based on each individual's lateral view, and analyzing the characteristics of their somatotype. Data were analyzed by factor analysis, cluster analysis, analysis of variance. Factor analysis was used to 23 items from photometric measurment and cluster analysis was applied for classification of upper torso forms. Through cluster analysis using 5 factor scores, 3 somatotypes were categorized from th lateral view 1) Type I was straight somatotype in which the plumb line passes throught the lobe of the ear, the shoulder joing and the mid abdominal region laterally. This type of woman was slender and shorter than average. 2) Type II was bending somatotype in which the upper portion of upper torso is bent forward. This type of woman was taller and fatter than average. 3) Type III was swayback somatotype in which the upper portion of protruding point on the back is bent forward but the lower portion of protruding point had characteristic of turning over somatotype. This type of woman had storter length on the front and longer lenght on the back, slender type and flat chest.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.8 s.145
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• pp.1125-1135
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• 2005

The purpose of this study were to clarify differences in information processing type in relation to the consumer knowledge and involvement of apparel and to clarify differences in demographic characteristics in relation to the information processing type of consumer. The subjects of this study were female adults who lived in Seoul, Kyunggi or Incheon areas and Quota sampling using age and residential areas was employed. Major statistical methods were Chi square test and discriminant analysis. The results were as follows: 1. Consumer knowledge was found to be significantly related to the classification of information processing type. Low knowledge group tended to process infarmation rationally but high knowledge group utilized both rational and experiential process. 2. Consumer involvement was found to be significantly related to the classification of information processing type. Low involvement group tended to process information passively. High involvement group utilized both rational and experiential process 3. Information processing type was related to consumer's demographic characteristics such as age, education level, marriage, and purchase expense of apparel

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.8
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• pp.1386-1397
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• 2001

The purpose of this study was to classify and analyze the somatotype of early middle-aged women and to provide its total data for clothing construction, and to improve clothing culture. The subjects were 277 early middle-aged women between 35 and 44 years old. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows; 1. The result of factor analysis indicated that 10 factors were extracted through factor analysis and those factors comprised 86.13 percent of total variance. 2. Using factor scores, cluster analysis was carried out and the subject were classified into 4 cluster. Type 1 is tall, slim, and X type in front. Type 2 is standard height and weight, short upper body, and hip-protruded on the side. Type 3 is standard height, thin, H type in front, back and hip are clearly protruded, and lean-back type on the side. Type 4 is standard height, fat, and long upper body. 3. According to the stepwise discriminant analysis, the 8 important iems is classifying the somatotype of early middle-aged women are as follows : bust girth, back length hip breadth-waist breadth, back protruded point depth(back)-back waist depth(back), hip tangent tilt, hip depth(back) waist dapth(back), bust depth-waist depth, and cervical hight, The correct classification rate for these items is as exact as 83.20%.