[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.56977/jicce.2022.20.4.288

Automatic Generation of Video Metadata for the Super-personalized Recommendation of Media

Yong, Sung Jung (Department of Computer Science and Engineering, Korea University of Technology and Education)
Park, Hyo Gyeong (Department of Computer Science and Engineering, Korea University of Technology and Education)
You, Yeon Hwi (Department of Computer Science and Engineering, Korea University of Technology and Education)
Moon, Il-Young (Department of Computer Science and Engineering, Korea University of Technology and Education)

Publication Information

Journal of information and communication convergence engineering / v.20, no.4, 2022 , pp. 288-294 More about this Journal

Abstract

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.

Keywords

AI; Metadata; OTT; Keyframe; YCbCr;

Citations & Related Records

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