• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.1
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• pp.227-243
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• 2017

This study proposed a structural and semantic framework for the characterization of events and segments in Web videos that permits content-based searches and dynamic video summarization. Although MPEG-7 supports multimedia structural and semantic descriptions, it is not currently suitable for describing multimedia content on the Web. Thus, the proposed metadata framework that was designed considering Web environments provides a thorough yet simple way to describe Web video contents. Precisely, the metadata framework was constructed on the basis of Chatman's narrative theory, three multimedia metadata formats (PBCore, MPEG-7, and TV-Anytime), and social metadata. It consists of event information, eventGroup information, segment information, and video (program) information. This study also discusses how to automatically extract metadata elements including structural and semantic metadata elements from Web videos.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.531-542
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• 2002

As most of metadata have been handled on restricted applications, we need a same metadata in order to represent a same video data. However, these metadata make problems that the same video data should be supported by the same metadata. Therefore, in this paper, we extend the Dublin core elements to support the metadata which can solve the problems. The proposed video data representation is managed by the extended metadata of Doblin core model, by using the information of structure, content and manipulation of video data. The thirteen temporal relationship operators are reduced to the six temporal relationship operators by using a dummy shot temporal transformation relationship. The reduced six temporal relationship operators through excluding reverse temporal relationship not only maintain a consistency of representation between a metadata and a video data, but also transform n-ary temporal relationship to binary relationship on shots. We show that the proposed metadata model can be applied to representing and retrieving on various applications as equivalent as the same structure.

• Journal of KIISE
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• v.42 no.3
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• pp.413-418
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• 2015

In this paper, we design new video metadata schema for searching video segments to create UEC (User Edited Contents). The proposed video metadata schema employs hierarchically structured units of 'Title-Event-Place(Scene)-Shot', and defines the fields of the semantic information as structured form in each segment unit. Since this video metadata schema is defined by analyzing the structure of existing UECs and by experimenting the tagging and searching the video segment units for creating the UECs, it helps the users to search useful video segments for UEC easily than MPEG-7 MDS (Multimedia Description Scheme) which is a general purpose international standard for video metadata schema.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.233-236
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• 2001

Due to the current limitation on network bandwidth, it is difficult to get the video files of interests from the server by downloading/streaming. To solve this problem, we propose a scheme for generating a virtual single video in the client side by downloading the selected video segments and the corresponding metadata from the server. Our system is based MPEG-7 standard on the multimedia metadata. The experimental system demonstrates the feasibility of our approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.453-455
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• 2022

Metadata is data that explains attributes and features to the data as structured data. Among them, video metadata refers to data extracted from information constituting the video for accurate content-based search. Recently, as the number of users using video content increases, the number of OTT providers is also increasing, and the role of metadata is becoming more important for OTT providers to recommend a large amount of video content to individual users or to search appropriately. In this paper, a study was conducted on a method of automatically extracting metadata for mood attributes through sound effects of images. In order to classify the sound effect of the video and generate metadata about the attributes of the mood, I would like to propose a method of establishing a terminology dictionary for the mood and extracting information through supervised learning.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2031-2041
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• 1999

In this paper, we present the HVIS (Hybrid Video Information System) which bolsters up meaning retrieval of all the various users by integrating feature-based retrieval and annotation-based retrieval of unformatted formed and massive video data. HVIS divides a set of video into video document, sequence, scene and object to model the metadata and suggests the Two layered Hybrid Object-oriented Metadata Model(THOMM) which is composed of raw-data layer for physical video stream, metadata layer to support annotation-based retrieval, content-based retrieval, and similarity retrieval. Grounded on this model, we presents the video query language which make the annotation-based query, content-based query and similar query possible and Video Query Processor to process the query and query processing algorithm. Specially, We present the similarity expression to appear degree of similarity which considers interesting of user. The proposed system is implemented with Visual C++, ActiveX and ORACLE.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.417-428
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• 2018

The intelligent video surveillance environment is a system that extracts various information about a video object and enables automated processing through the analysis of video data collected in CCTV. However, since the privacy exposure problem may occur in the process of intelligent video surveillance, it is necessary to take a security measure. Especially, video metadata has high vulnerability because it can include various personal information analyzed based on big data. In this paper, we propose a COP-Transformation scheme to protect video metadata. The proposed scheme is advantageous in that it greatly enhances the security and efficiency in processing the video metadata.

• Journal of Korea Multimedia Society
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• v.1 no.2
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• pp.131-141
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• 1998

Video data occurred in multimedia documents consist of a large scale of irregular data including audio-visual, spatial-temporal, and semantic information. In general, it is difficult to grasp the exact meaning of such a video information because video data apparently consist of unmeaningful symbols and numerics. In order to relieve these difficulties, it is necessary to develop an integrated manager for complex structures of video data and provide users of video digital libraries with easy, systematic access mechanisms to video informations. This paper proposes a generic integrated video information model(GIVIM) based on an extended Dublin Core metadata system to effectively store and retrieve video documents in digital libraries. The GIVIM is an integrated mo이 of a video metadata model(VMN) and a video architecture information model(VAIM). We also present design and implementation results of a video document management system(VDMS) based on the GIVIM.