• Journal of Digital Contents Society
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• v.4 no.1
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• pp.45-55
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• 2003

Due to the rapid development of information & communication technology under high performance computing environments, the multimedia production techniques have been applied to a variety of multimedia fields such as general banner advertisements including texts, images and animations, and the internet-broadcasting dealing with videos and sounds. This paper presents an authoring tool with main functions to setup events objects (image, animation, sound, button, area) and to setup action functions, so that non-experts can easily produce multimedia including images, sounds, animations and so on. The authoring tool implemented in Java can be applied to the CD-ROM title production as well as the web-site construction. We can expect that when this authoring tool is used for in multimedia production, both cost and time will be reduced due to its convenience and powerful functions. We have a future plan to integrate intelligent multimedia presentation techniques with the presented tool for the autonomous multimedia authoring works.

• Journal of IKEEE
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• v.24 no.3
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• pp.743-753
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• 2020

In this paper, we propose a set of robustness indicators for immersive 360-degree video. With the full-fledged service of mobile carriers' 5G networks, it is possible to use large-capacity, immersive 360-degree videos at high speed anytime, anywhere. Since it can be illegally distributed in web-hard and torrents through DRM dismantling and various video modifications, however, evaluation indicators that can objectively evaluate the filtering performance for copyright protection are required. In this paper, a robustness indicators is proposed that applies the existing 2D Video robustness indicators and considers the projection method and reproduction method, which are the characteristics of Immersive 360-degree Video. The performance evaluation experiment has been carried out for a sample filtering system and it is verified that an excellent recognition rate of 95% or more has been achieved in about 3 second execution time.

• Journal of KIISE
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• v.43 no.4
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• pp.470-479
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• 2016

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.482-484
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• 2017

Over the past 70 years, movies and television have revolutionized the way people communicate. However, even with this development, TV has been used only as a means of communication targeting an unspecified number of people due to the restriction of media such as radio waves and movies. However, the development of the Internet and online video has come to a time when 100 million people watch YouTube videos uploaded from the other side of the world by eliminating these restrictions. The message that you want to deliver now can be delivered to anyone, but making the image with these messages remains the last obstacle to communication. To solve these problems, we implemented a web application and a video production program through AWS. This system basically provides the administrator with the video production through the easy interface, the information management and the program on the server on the internet through AWS, the assigned lecture including the computer and the smart phone, the learning materials, And implemented to increase the efficiency of educational video production service.

• Journal of Digital Convergence
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• v.9 no.2
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• pp.131-140
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• 2011

As the recent advances in network and wireless communications and semiconductor design and process technologies, our computing platform is rapidly shifting from desktop PCs to mobile devices such as UMPC (Ultra Mobile PC), Tablet PC, and Smartphone. Especially, wide-spreading Smartphones allow a new field of application based on location based services available with an user interface called augmented reality (AR). Therefore, this paper introduces an implementation of AR using various OpenAPls on Android Smartphones. In order to utilize enrich user data in real time, the system integrates with location based social network services also with OpenAPI. These APIs enable third-party developers to make use of rich contents of many portal web sites. The prototype was implemented on the real Android phone, Sky Sirius, and the result shows that it can provide an efficient location based service using AR technology without any constraints on mobile devices; in addition, it connects SNS to AR for sharing user data including photos, videos, and messages based on a specific location.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1403-1410
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• 2020

Recently, as interest in non-face-to-face experiences and services increases, the demand for web video contents that can be easily consumed using mobile devices such as smartphones or tablets is rapidly increasing. To cope with these requirements, in this paper we propose a technique to efficiently produce video contents that can provide experience of visiting famous places (i.e., stage tour) in animation or movies. To this end, an image dataset was established by collecting images of stage areas using Google Maps and Google Street View APIs. Afterwards, a deep learning-based style transfer method to apply the unique style of animation videos to the collected street view images and generate the video contents from the style-transferred images was presented. Finally, we showed that the proposed method could produce more interesting stage-tour video contents through various experiments.

• Journal of dental hygiene science
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• v.21 no.2
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• pp.104-110
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• 2021

Background: Regardless of countries, the myth that rain makes the body ache has been worded in various forms, and a number of studies have been reported to investigate this. However, these studies, which depended on the patient's experience or memory, had obvious limitations. Google Trends is a big data analysis service based on search terms and viewing videos provided by Google LLC, and attempts to use it in various fields are continuing. In this study, we endeavored to introduce the 'value as a research tool' of the Google Trends, that has emerged along with technological advancements, through research on 'whether toothaches really occur frequently on rainy days'. Methods: Keywords were selected as objectively as possible by applying web crawling and text mining techniques, and the keyword "bi" meaning rain in Korean was added to verify the reliability of Google Trends data. The correlation was statistically analyzed using precipitation and temperature data provided by the Korea Meteorological Agency and daily search volume data provided by Google Trends. Results: Keywords "chi-gwa", "chi-tong", and "chung-chi" were selected, which in Korean mean 'dental clinic', 'toothache', and 'tooth decay' respectively. A significant correlation was found between the amount of precipitation and the search volume of tooth decay. No correlation was found between precipitation and other keywords or other combinations. It was natural that a very significant correlation was found between the amount of precipitation, temperature, and the search volume of "bi". Conclusion: Rain seems to actually be a cause of toothache, and if objective keyword selection is premised, Google Trends is considered to be very useful as a research tool in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.44-49
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• 2021

Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.193-207
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• 2021

As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality. This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This study has implications in that it suggests the fitness and evaluation criterion of the Deepfake dataset. Since the evaluation criterion presented in this study was derived based on the items considered in previous studies, it is thought that all evaluation criterions will be effective for quality improvement. It is also expected to be used as criteria for selecting an appropriate deefake dataset or as a reference for designing a Deepfake data benchmark. This study could not apply the presented evaluation criterion to existing Deepfake datasets. In future research, the proposed evaluation criterion will be applied to existing datasets to evaluate the strengths and weaknesses of each dataset, and to consider what implications there will be when used in Deepfake research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.17-22
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• 2022

With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.