Photo filter recommendation through analyzing objects, scenes and aesthetics

Yi Ning Chen, Mei Chen Yeh

研究成果: 書貢獻/報告類型會議貢獻

摘要

Photo filters are widespread - they give photos a stylized look without requiring the user to have professional knowledge of image processing. However, with the increasing number of photo filters and limited display size of mobile devices, selecting a proper filter for a given photo can be a tedious task for camera phone users. In this paper, we present a photo filter recommendation approach to address this problem. In particular, we rely on state-of-the-art deep learning approaches to extract objects, scenes, and image aesthetics reliably and represent them using deep features as the observations of our recommendation model. Furthermore, we collect 68,400 filtered photos from Instagram to learn the relationships among objects, scenes, aesthetics, and filter types. Experimental results using the FACD benchmark dataset demonstrate the state-of-the-art recommendation performance of the proposed approach; these results show that objects, scenes, and aesthetic attributes influence filter preference.

原文英語
主出版物標題Proceedings - 2019 IEEE 5th International Conference on Multimedia Big Data, BigMM 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面252-256
頁數5
ISBN(電子)9781728155272
DOIs
出版狀態已發佈 - 2019 九月
事件5th IEEE International Conference on Multimedia Big Data, BigMM 2019 - Singapore, 新加坡
持續時間: 2019 九月 112019 九月 13

出版系列

名字Proceedings - 2019 IEEE 5th International Conference on Multimedia Big Data, BigMM 2019

會議

會議5th IEEE International Conference on Multimedia Big Data, BigMM 2019
國家新加坡
城市Singapore
期間2019/09/112019/09/13

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Media Technology

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