• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Korea Science and Art Forum
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• v.29
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• pp.279-292
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• 2017

This study was started by recognizing that visual storytelling(VST) is an important factor that determines the success of the work. The goal of this study is to analyze the VST study approaching from the narrative and visual dimension by analyzing the conflict intensity and VST factor. Therefore, in this paper, we analyzed the conflicts of the theater animation(4) that succeeded in the worldwide success and attempted the VST interpretation by approaching it technically. The results and contents of the study are as follows. Firstly, based on the narrative theory of Sung bong-Sun and Robert McKee, we classified the conflict scenes and found the kinds of conflicts. In addition, based on the 5B model, a total of 108 conflict shots were extracted. Secondly, through expert experiment, we found the conflict intensity of conflict shots. Thirdly, the visual elements of fifteen significant conflicts were extracted from internal and super individual conflicts. Fourth, as a result of the experiment, it was confirmed that the reliability of the visual elements in the inner and super personal conflicts was in the range of 100-83.33%, and the frequency of usage was found to be widely distributed in 5.88-70.59% and 5-70%. This means that the VST expression, which relied on the sense of the artist, can be engineered. Finally, I expect that it will be the basis of the development of the VST Tool which can predict the conflict expression of the work in the animation pre - production stage successfully.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.825-834
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• 2023

The demands of people who purchase fashion products on Internet shopping are gradually increasing, and attempts are being made to provide user-friendly images with 3D contents and web 3D software instead of pictures and videos of products provided. As a reason for this issue, which has emerged as the most important aspect in the fashion web shopping industry, complaints that the product is different when the product is received and the image at the time of purchase has been heightened. As a way to solve this problem, various image processing technologies have been introduced, but there is a limit to the quality of 2D images. In this study, we proposed an automatic conversion technology that converts 2D images into 3D and grafts them to web 3D technology that allows customers to identify products in various locations and reduces the cost and calculation time required for conversion. We developed a system that shoots a mannequin by placing it on a rotating turntable using only 8 cameras. In order to extract only the clothing part from the image taken by this system, markers are removed using U-net, and an algorithm that extracts only the clothing area by identifying the color feature information of the background area and mannequin area is proposed. Using this algorithm, the time taken to extract only the clothes area after taking an image is 2.25 seconds per image, and it takes a total of 144 seconds (2 minutes and 4 seconds) when taking 64 images of one piece of clothing. It can extract 3D objects with very good performance compared to the system.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Cartoon and Animation Studies
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• s.37
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• pp.107-142
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• 2014

The purpose of this study is to examine the effects of the teaching English factors on student's communicative competence and motivation by using animation at the College. To achieve this purpose, this study presented an effective integrative teaching model to develop students communicative competence. The study created animation based teaching English model by using the animation of Frozen and applied it to lectures. Using animation in the classroom was a creative English teaching technique involving authentic activities like English dram, English guide contest, and various communicative activities A case study on the use of the animation in English classes at was examined and the language teaching syllabus were provided. In order to investigate the motivation and proficiency of learners, the writer chose 79 students who took the lecture. The study discovered the students' motivation and proficiency in English improved significantly. The results of experiment are as follows: First, using animation in the English class was found to have meaningful influence student's intrinsic motivation to learn English. Second, using animation in the English class was found to be effective for developing student's English proficiency. Third, appropriate materials should be selected and applied it to the real classroom activities. In conclusion, one of disadvantages of learning is less communication and the authentic interaction in a real life, so that the integrative teaching methodology which is combined English content and English animation content is also the effective method to improve student's intrinsic motivations in the age of global village.

• Korean journal of communication and information
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• v.57
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• pp.202-226
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• 2012

The purpose of this study is to analyze how Chinese people perceive the acceptance and spread of Korean popular culture through the analysis of the Korean Wave news frame in Chinese daily newspapers. The result of analysis showed that there was a significant difference in the number of an article appearance in all three daily newspapers by year according to a specific issue including the popularity of Korean Wave-related events, Korean dramas, movies and Korean stars. This reporting trend also appeared in the article format, and all three daily newspapers mainly used an episodic frame to deliver fragmentary Korean Wave trend or personal information emotionally rather than a thematic frame to analyze and interpret the Korean Wave trend in depth. As a result of analyzing dominant news frames using the inductive approach to examine specific contents of the articles, news frames that composed the Korean Wave into the cultural interest frame as 'Interest and attention toward Korean popular culture' and gave positive evaluations were dominant, followed by the economic news frames that explained the Korean Wave from the economic perspective and the political news frames that considered the Korean Wave from the viewpoint of the Chinese Government. These news frames appeared somewhat different according to the publishing purpose of daily newspapers. ${\ll}$People's Daily${\gg}$ which is the official organ of the Chinese Communist Party often covered the political frames to report the policies of Chinese government, ${\ll}$People's Daily (Overseas edition)${\gg}$ often covered economic frames from the overseas perspective and the Jinghua Daily which is a commercial newspaper by nature often handled cultural interest frames to consider Korean Wave as new cultural phenomenon.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.479-488
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• 2002

In recent years the amount of digital video used has risen dramatically to keep pace with the increasing use of the Internet and consequently an automated method is needed for indexing digital video databases. Textual information, both superimposed and embedded scene texts, appearing in a digital video can be a crucial clue for helping the video indexing. In this paper, a new method is presented to extract both superimposed and embedded scene texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, a color image is converted into a gray-level image and applies contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose+CloseOpen)/2 morphological operations, maintaining text components using (OpenClose+CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, my method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Archives of design research
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• v.19 no.5 s.67
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• pp.119-128
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• 2006

The scale of visual expression has expanded from freeze frame to motion picture as media have developed. Moving pictures such as animation, movies, TV CM and GUI become formative elements whose movement is necessary compared to freeze frame as apparent movement phenomenon and unit structure such as short and scene appear. Therefore, of formative elements such as a shape, color, space, size and movement, movement is importantly distinguished in the moving image. The expression and form of image as a relationship between the signified and signifier explained by Saussure are accepted as a sign by mutual complement even though they limit the content. This makes it possible to infer that the formal feature of movement participates in the message content. To verify this, the result of moving picture visual perception experiment based on the gestalt grouping principle result shows that 70-80 percent of subjects think that 'movement' is the important grouping clue in perception. Movement affects the maintenance of the context of message content in the communication process when the meaning structure of moving picture is analyzed based on the structural feature. The identity can be maintained with if there is a movement with similar directive point even if the color and shape of people, things and background are changed. Second, the clarity of the content is elevated by a distinguished object as a figure by movement. Third, it acts as a knowledge representation which can predict similar movement process of next information processing. Forth, movement gives the content consistency even though more than two scenes have fast switch and complicated editing structure like cross-cutting. Movement becomes a clue which can make grouping information input by visual perception reaction. Also, it gives the order to the visual expression which can be used improperly by formation of structural frame of image message and has the effectiveness which elevates the clarity of signification. Moving picture has discourse with several mixed unit structures because it fundamentally contains time and the common and distinguished expression is needed by media-mix circumstances. Therefore, by the application of gestalt grouping principle to moving picture field, movement becomes the more distinguished than other formative elements and affects the formation of meaning structure. This study propose a viewpoint that develops structural formative beauty and new image expression in the media image field.