Abstract
This paper is a paper conceived to improve the Cultural Image Frame Network (CIFN) by utilizing deep learning-based image learning datasets that have recently received much attention in major research fields such as computer vision and Natural Language Processing (NLP). In particular, CNN, which uses convolutional filters for images to calculate quickly and considers the entire image, including specific objects as well as backgrounds, is a very suitable algorithm for extracting cultural elements that constitute the cultural image frame of this culture mining study. In addition, by utilizing images in the form of refined images verified with deep learning experimental and test datasets, the limitations of existing research, such as (1) reliability of tagging information, (2) inaccuracy of the segmentation method, and (3) redundancy of images, can contribute to more sophisticated research.
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