| Title |
A Framework of Automatic Ontology Construction based on Scene Graph Generation Model for Analysis of Story Video Contents |
| Authors |
강동구(Donggu Kang) ; 김지연(Jiyeon Kim) ; 정종진(Jongjin Jung) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.9.1286 |
| Keywords |
Deep Learning; Scene Graph; Ontology; Protege; Story Video Content |
| Abstract |
In this paper, we propose an extended ontology automatic construction framework based on multiple deeplearning models as part of an effective analysis of story video content. Semi-automatic techniques usingimage processing techniques have been the mainstream for the existing ontology construction methods formoving pictures, but there is a problem that human intervention is required and the accuracy is low due tothe limitations of image processing techniques. To overcome this, in this paper, we propose an automatedmethod based on the deep learning scene graph generation technique. In particular, in the case of videocontent with a story, the relationship between characters is a very important factor in understanding thescene, so deep learning-based object relationship creation model, character identification model, andimportant area caption generation model are applied to extract objects and recognize their relationships.And design a framework that automatically builds a domain ontology dependent on the story throughprocedural fusion between each model and module function. In addition, the proposed framework suggests amethod for efficiently processing system requirements and system resources through meta control in thecondition that requires simultaneous operation of multiple deep learning models to analyze story videocontent. Through this, the proposed framework effectively identifies critical region captions and objectrelationships in a scene in story-telling video content, and executes three types of models simultaneously.Finally, we conduct an experiment to automatically build an ontology by applying the proposed framework tospecific video content, and check the effectiveness of the proposed framework. |