• Korea Science and Art Forum
• /
• v.37 no.3
• /
• pp.125-134
• /
• 2019

This study was conducted to organize a program to classify and analyze the characteristics of images for the automation of background music selection in the video content production process. The results and contents of the study are as follows: video characteristics are selected as subject category, emotion, pixel motion speed, color, and character material. Subject categories and feelings were extracted using Microsoft's Azure Video Indexer, Pixel Movement Speed was an Optional flow, Color was an Image Histogram for Image, and character materials was CNN(Convolutional Neural Network). The results of this study are significant in that video analysis was conducted to match background music in the recent content production process of 'Internet One-person Broadcasting Creators'.

• The Journal of the Korea Contents Association
• /
• v.19 no.9
• /
• pp.195-211
• /
• 2019

Recently, as the personal broadcasting industry has grown rapidly, interest in personal braodcasting platform companies, creators, and contents is increasing. In addition to the quantitative growth of the private broadcasting industry, related research is also increasing, but there is a lack of research on the viewer side. In this study, we applied the Hedonic-Motivation System Adoption Model (HMSAM) to derive the viewing motivation factors from the intrinsic aspects of personal broadcasting users. Also, we analyzed the difference of viewing motivation factors by classifying the types of personal broadcasting contents. As a result of the analysis, extrinsic and intrinsic factors of the users except "control" has a significant influence on the viewing intention and immersion. Also, it was verified that the influences on the viewer's immersion were different according to the content type. Finally, various theoretical and practical implications are presented through the results of this study.