文章基本信息

标题：A core region captioning framework for automatic video understanding in story video contents
本地全文：下载
作者：Hyesun Suh ; Jiyeon Kim ; Jinsoo So 等
期刊名称：International Journal of Engineering Business Management
印刷版ISSN：1847-9790
电子版ISSN：1847-9790
出版年度：2022
卷号：14
DOI：10.1177/18479790221078130
语种：English
出版社：InTech
摘要：Due to the rapid increase in images and image data, research examining the visual analysis of such unstructured data has recently come to be actively conducted. One of the representative image caption models the DenseCap model extracts various regions in an image and generates region-level captions. However, since the existing DenseCap model does not consider priority for region captions, it is difficult to identify relatively significant region captions that best describe the image. There has also been a lack of research into captioning focusing on the core areas for story content, such as images in movies and dramas. In this study, we propose a new image captioning framework based on DenseCap that aims to promote the understanding of movies in particular. In addition, we design and implement a module for identifying characters so that the character information can be used in caption detection and caption improvement in core areas. We also propose a core area caption detection algorithm that considers the variables affecting the area caption importance. Finally, a performance evaluation is conducted to determine the accuracy of the character identification module, and the effectiveness of the proposed algorithm is demonstrated by visually comparing it with the existing DenseCap model.