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http://dx.doi.org/10.6109/jkiice.2020.24.5.674

Analysis of Cultural Context of Image Search with Deep Transfer Learning  

Kim, Hyeon-sik (Department of Computer Engineering, Kumoh National Institute of Technology)
Jeong, Jin-Woo (Department of Computer Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.5, 2020 , pp. 674-677 More about this Journal
Abstract
The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.
Keywords
Cultural Difference; Deep Learning; Image Search; Transfer Learning;
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