• Fashion & Textile Research Journal
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• v.7 no.3
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• pp.291-300
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• 2005

The purpose of this study is to investigate, analyze the purchasing behavior of fashion goods according to life style and role model of preteen generation, and provide manager or marketing planner for the reference data so that they can understand preteen generation and make proper strategy efficiently. So called, preteen market focusing on 1014 generation (from ten to fourteen years old) is highlighted. This generation created between the year 1989 to 1993 after Seoul Olympic monopolize parent's love in abundant economic environment and rise to the core of consumption subject. Products aiming at this preteen generation continuously though consumption mind was shrunk greatly due to recession. Only 2~3 years before preteen market was regarded as grey zone which doesn't belong to not only children (between six and nine years old) but also teenagers (between fifteen to eighteen years old). But in recent day their purchasing powers have increased rapidly and age group is divided on details, so that preteen market has become a niche market. Subjects were 333 persons consisting of students in the 4th~6th grade of primary school and the 1st~2nd grade of middle school in Daegu city. Measuring instruments are as follows: 5questions to differentiate preteen generation, 22 questions to measure life style, 17questions (which have six sub-factors such as purchase motive, factor of product selection, utilization of informant, purchase time, purchase place, and purchase method) to measure the purchase behavior of fashion goods measurement, and 16 questions (which have four sub-factors such as parent, entertainer & sports stars, brothers and sisters, friends) to measure model of role. Statistical data were processed by SPSS 10.0 programs. Frequencies, Factor analysis, Cluster analysis, ANOVA, Cross analysis, Multiplex regression analysis, and Duncan's multiple range test were carried out.

• The Korean Journal of Community Living Science
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• v.19 no.3
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• pp.431-444
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• 2008

Today people obtain much information about appearances through movies. The stars' body images in movies have been created through total fashion as they play the roles of an ideal model to create actual body images. This study examined the relations between the body images in the film media in terms of appearances or total fashion and actual body images based on the simulation theory by Jean Baudrillard. It conducted a literature study by collecting and analyzing film media-related picture images from books, papers, periodicals and the Internet home and abroad. The research scope was limited to the stars who made appearances in movies and the ones who starred in movies in addition to their other lines of work. As a result, the following conclusions were drawn; the stars' body images went hyperreal between their actual body images and the ones of their roles in movies and then were simulated in fashion. To be specific, 1. the stars' actual body images went hyperreal and were simulated in fashion in the following cases; 1) the stars in the heyday of film; 2) fashionistas; and 3) pictures taken by paparazzi. 2. The body images of their roles went hyperreal in the following cases; 1) through brand participation; 2) through changed body images; and 3) through cyber body images. Their body images became hyperreal and were simulated in fashion by the input of fashion designers and the techniques of other areas. The body images in film are the object of desire to the audience. They go hyperreal and become the ideal body image to real people. The various kinds of hyperreal images in the film media create new beauty as a reference and object of comparison for people to change their body images in reality.

• Journal of the Korean Society of Fashion and Beauty
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• v.2 no.3 s.3
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• pp.11-18
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• 2004

The purpose of this study was to review the origin and background of well-being, the emerging well-being culture, and the phenomenon and features of the hair beauty affected by it. For this purpose, the researcher reviewed dissertations written between 199a and 2002, copies of the beauty journal Short Hair published between 2002 and 2003, beauty newspapers published between March, 2003 and June, 2004, and relevant Internet sites. When we review the well-being hair style tendency, we may be reminded of a light shaggy cut style with a thick wave volume or a natural wave highlighted, or a natural style with a natural color rendered by the braid technique. The well-being hair styles may be inspired by such natural images as the earth, sand, rock, wind, feather and flowers, while using cut, permanent wave or coloring technique for the hairs, in order to create new hair styles. The goal is to make men in contact with the nature. Meanwhile, as people are more concerned about diverse hair textures, natural and healthy hairs, they want their hairs to look comfortable and active rather than luxurious. The well-being cut styles may be categorized into cut style highlighting healthy hairs with a Bobos luxury and soft and light stroke cut reminiscent of a Bohemian freedom. The well-being permanent style may be divided into thick wave, natural wave, and the permanent style using a strong wave to make hairs look bloated. The hair colors reminding us of some natural images are used to highlight a well-being image. In addition, the concept of well-being pursuing a happy and healthy life has been being introduced into the permanent wave material liquids; such liquids made from natural materials, fruits or herbs increase, while environmental-friendly liquids or materials are used more for healthy hairs. The reason why the well-being culture influences hair styles importantly may be that people's desire of healthy body and mind and a comfortable life is reflected in hair styles. In conclusion, as the hair styles or an index for a new cultural current in our modern age are influenced by the well-being phenomenon, more and more people will put priority on healthy hairs. So, it is deemed necessary to apply the well-being hair styles appropriately to render satisfactory hair styles.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of the Korean association of regional geographers
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• v.22 no.2
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• pp.307-330
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• 2016

This article aims to explain how Gangnam, as a model and standard of compressed urbanization in South Korea, was created. Gangnam and Gangnam-style urbanization need attention not only because they contrast with Korea's urbanization in the past as well as urbanization in the West but also they provide an important model in contemporary Korea's politics, economy and culture. However, there are little studies of how Gangnam's peculiar urbanism was created. To fill this gap, this article will first capture Gangnam's peculiar urbanism as a material landscape and sociocultural lifestyle. Gangnam-style urbanism is (a) materially characterized by high-rise apartment complexes owned by the middle and upper class for dwelling and asset growth and (b) socio-culturally characterized by political conservatism, public indifference, competition over academic performance, appearance, and fashion, and nightlife. Then it will show Gangnam's archetype was created in a spatially and temporally compressed way in and through the spatial selectivity of Korean anti-communist authoritarian developmental state strategies: (1) anti-communism led to the diffusion and accommodation of the population through apartments in Gangnam in the context of its confrontation with North Korea and the fast-growing population of Seoul; (2) military authoritarianism excluded the low-income class and the urban poor from urban development; and (3) the developmental state adopted selective housing policy which treated construction companies and the middle class preferentially through exceptional zoning and price distortions, promoting the construction of apartment in Gangnam and its resultant uneven development.