Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Cultural Context of Image Search with Deep Transfer Learning

심층 전이 학습을 이용한 이미지 검색의 문화적 특성 분석

  • Kim, Hyeon-sik (Department of Computer Engineering, Kumoh National Institute of Technology) ;
  • Jeong, Jin-Woo (Department of Computer Engineering, Kumoh National Institute of Technology)
  • Received : 2020.03.25
  • Accepted : 2020.04.07
  • Published : 2020.05.31
Download PDF
⟨ Previous Next ⟩

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

References

  1. I. Marina, P. Miran, M. Luka, "Movie posters classification into genres based on low-level features," in Proceeding of the 37th International Convention on Information and Communication Technology, pp. 1198-1203, 2014.
  2. S. Jang, J. Wi, Y. Kim, "Korean Movie Genre Classification based on Poster Images with Multi-label Training using CNN," in Proceeding of the HCI Society of Korea, pp. 746-749, 2019.
  3. R. Miriam, C. Damon, L. Manovich, O. Simon, "What Makes Photo Cultures Different?," in Proceeding of ACM International Conference on Multimedia, pp. 287-291, 2016.
  4. C. Yu-Ting, C. Wen-Haung, W. Bo, H. Kai-Lung, "Fashion World Map: Understanding Cities Through Streetwear Fashion," in Proceeding of the 25th ACM International Conference on Multimedia, pp. 91-99, 2017.
  5. S. Karen, Z. Andrew, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in Proceeding of International Conference on Learning Representations, pp.1-14, 2015.
  6. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proceedings of Computer Vision and Pattern Recognition, pp.4510-4520, 2018.
  7. C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, C. Liu, "A Survey on Deep Transfer Learning," in Proceeding of International Conference on Artificial Neural Networks, pp.270-279, 2018.