• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.347-353
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• 2022

In this paper, we propose a method that performs a text-video retrieval model by replacing video properties using captions. In general, the exisiting embedding-based models consist of both joint embedding space construction and the CNN-based video encoding process, which requires a lot of computation in the training as well as the inference process. To overcome this problem, we introduce a video-captioning module to replace the visual property of video with captions generated by the video-captioning module. To be specific, we adopt the caption generator that converts candidate videos into captions in the inference process, thereby enabling direct comparison between the text given as a query and candidate videos without joint embedding space. Through the experiment, the proposed model successfully reduces the amount of computation and inference time by skipping the visual processing process and joint embedding space construction on two benchmark dataset, MSR-VTT and VATEX.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.215-220
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• 2022

Emotion recognition is one of the most important and challenging areas of computer vision. Nowadays, many studies on emotion recognition were conducted and the performance of models is also improving. but, more research is needed on emotion recognition and sentiment analysis of video viewers. In this paper, we propose an emotion analysis system the includes a sentiment analysis model and an interest prediction model. We analyzed the emotional patterns of people watching popular and unpopular videos and predicted the level of interest using the emotion analysis system. Experimental results showed that certain emotions were strongly related to the popularity of videos and the interest prediction model had high accuracy in predicting the level of interest.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.713-715
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• 2022

Video learning through YouTube has emerged as one of the most widely used instructional methods, yet relatively little research has been conducted on YouTube video users' willingness to use or behavior, so it is important to examine how to make and keep users' willingness to continue learning and to improve their retention rate for effective online learning. With reference to perceived value theory and utilizing an ECM perspective, the authors construct a model of YouTube video continuous learning intention and investigate the influence of perceived value and satisfaction on users' willingness to use YouTube videos for continuous learning.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.12-21
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• 2023

Visual relationship understanding in computer vision allows to recognize meaningful relationships between objects in a scene. This technology enables the extraction of representative information within visual content. We discuss the technology of visual relationship understanding, specifically focusing on videos. We first introduce visual relationship understanding concepts in videos and then explore the latest existing techniques. Next, we present benchmark datasets commonly used in video visual relationship understanding. Finally, we discuss future research directions in video visual relationship understanding.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.886-887
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• 2023

This paper is about the development of kiosks that provide four types of service. Simple UI and educational videos solve the complexity of existing kiosks and provide an intuitive and convenient screen to users. In addition, the AR function, which is a three-dimensional form, shows directions and store representative images. After storing user information in the DB, a learning model is generated using user-based KNN collaborative filtering to provide a recommendation menu. As a result, it is possible to increase user convenience through kiosks using metaverse and AI recommendation services. It is also expected to solve digital alienation of social classes who have difficulty using kiosks.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.79-86
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• 2023

In our research, we delved into the impact of video length assigned for pre-class assignments on students' level of engagement. What we discovered is that as the length of the video increases, student engagement tends to decrease and the time allocated for homework preparation does not significantly influence engagement, as many students tend to complete their assignments just before the due date. Interestingly, the well-known "6-minute rule" often advocated for online educational videos does not align with the dynamics of real university settings. Whether in traditional lecture-based classes or flipped learning environments, students exhibit a high degree of self-responsibility when it comes to video consumption. Our findings strongly suggest that, in the context of flipped learning, it is advisable to create videos that are shorter than 15 minutes in length.

• The Korean Journal of Gastroenterology
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• v.72 no.6
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• pp.275-275
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• 2018

• Journal of Digital Contents Society
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• v.12 no.4
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• pp.449-456
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• 2011

UCC (User Created Contents) video rapidly rose as key contents of internet pop culture as a medium of 'sensibility delivery' that is created by a user without a commercial purpose. 'Sensibility delivery' is the ultimate purpose of UCC video and it can only be completed with 'well composed' storytelling. However, it is difficult to expect high degree of completion from noncommercial UCC videos due to an instable production environment and many variables. Thus, this study has been suggested 'detailed storytelling editing' process from editing phase in order to minimize problems of instable UCC videos. This suggestion based on actual results and award winning works of UCC videos of a team of research will raise a bond of sympathy, lead creative change, and expand the function of 'sensibility delivery' by increasing the degree of completion of work with unified final cut.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1258-1270
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• 2017

'Semantic gap' has been a year-old problem in automatic video summarization, which refers to the gap between semantics implied in video summarization algorithms and what people actually infer from watching videos. Using the external EEG bio-feedback obtained from video watchers as a solution of this semantic gap problem has several another issues: First, how to define and measure noises against ERP waveforms as signals. Second, whether individual differences among subjects in terms of noise and SNR for conventional ERP studies using still images captured from videos are the same with those differently conceptualized and measured from videos. Third, whether individual differences of subjects by noise and SNR levels help to detect topic-irrelevant shots as signals which are not matched with subject's own semantic topical expectations (mis-match negativity at around 400m after stimulus on-sets). The result of repeated measures ANOVA test clearly shows a 2-way interaction effect between topic-relevance and noise level, implying that subjects of low noise level for video watching session are sensitive to topic-irrelevant visual shots, while showing another 3-way interaction among topic-relevance, noise and SNR levels, implying that subjects of high noise level are sensitive to topic-irrelevant visual shots only if they are of low SNR level.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.307-318
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• 2016

비디오 프레임 내의 오버레이 텍스트는 음성과 시각적 내용에 부가적인 정보를 제공한다. 특히, 뉴스 비디오에서 이 텍스트는 비디오 영상 내용을 압축적이고 직접적인 설명을 한다. 그러므로 뉴스 비디오 색인 시스템을 만드는데 있어서 가장 신뢰할 수 있는 실마리이다. 텔레비전 뉴스 프로그램의 색인 시스템을 만들기 위해서는 텍스트를 검출하고 인식하는 것이 중요하다. 이 논문은 뉴스 비디오에서 오버레이 텍스트를 검출하고 인식하는데 도움이 되는 오버레이 텍스트 시작 프레임 식별을 제안한다. 비디오 시퀀스의 모든 프레임이 오버레이 텍스트를 포함하는 것이 아니기 때문에, 모든 프레임에서 오버레이 텍스트의 추출은 불필요하고 시간 낭비다. 그러므로 오버레이 텍스트를 포함하고 있는 프레임에만 초점을 맞춤으로써 오버레이 텍스트 검출의 정확도를 개선할 수 있다. 텍스트 시작 프레임 식별 방법에 대한 비교 실험을 뉴스 비디오에 대해서 실시하고, 적절한 처리 방법을 제안한다.