Learning to blend photos

Wei Chih Hung, Jianming Zhang, Xiaohui Shen, Zhe Lin, Joon Young Lee, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Photo blending is a common technique to create aesthetically pleasing artworks by combining multiple photos. However, the process of photo blending is usually time-consuming, and care must be taken in the process of blending, filtering, positioning, and masking each of the source photos. To make photo blending accessible to general public, we propose an efficient approach for automatic photo blending via deep learning. Specifically, given a foreground image and a background image, our proposed method automatically generates a set of blending photos with scores that indicate the aesthetics quality with the proposed quality network and policy network. Experimental results show that the proposed approach can effectively generate high quality blending photos with efficiency.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsVittorio Ferrari, Cristian Sminchisescu, Martial Hebert, Yair Weiss
PublisherSpringer Verlag
Pages72-88
Number of pages17
ISBN (Print)9783030012335
DOIs
Publication statusPublished - 2018 Jan 1
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 2018 Sep 82018 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11211 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Computer Vision, ECCV 2018
CountryGermany
CityMunich
Period18/9/818/9/14

Fingerprint

Masking
Learning
Positioning
Filtering
Experimental Results
Policy
Aesthetics
Background
Deep learning

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hung, W. C., Zhang, J., Shen, X., Lin, Z., Lee, J. Y., & Yang, M. H. (2018). Learning to blend photos. In V. Ferrari, C. Sminchisescu, M. Hebert, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings (pp. 72-88). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11211 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01234-2_5
Hung, Wei Chih ; Zhang, Jianming ; Shen, Xiaohui ; Lin, Zhe ; Lee, Joon Young ; Yang, Ming Hsuan. / Learning to blend photos. Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. editor / Vittorio Ferrari ; Cristian Sminchisescu ; Martial Hebert ; Yair Weiss. Springer Verlag, 2018. pp. 72-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Hung, WC, Zhang, J, Shen, X, Lin, Z, Lee, JY & Yang, MH 2018, Learning to blend photos. in V Ferrari, C Sminchisescu, M Hebert & Y Weiss (eds), Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11211 LNCS, Springer Verlag, pp. 72-88, 15th European Conference on Computer Vision, ECCV 2018, Munich, Germany, 18/9/8. https://doi.org/10.1007/978-3-030-01234-2_5

Learning to blend photos. / Hung, Wei Chih; Zhang, Jianming; Shen, Xiaohui; Lin, Zhe; Lee, Joon Young; Yang, Ming Hsuan.

Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. ed. / Vittorio Ferrari; Cristian Sminchisescu; Martial Hebert; Yair Weiss. Springer Verlag, 2018. p. 72-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11211 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Photo blending is a common technique to create aesthetically pleasing artworks by combining multiple photos. However, the process of photo blending is usually time-consuming, and care must be taken in the process of blending, filtering, positioning, and masking each of the source photos. To make photo blending accessible to general public, we propose an efficient approach for automatic photo blending via deep learning. Specifically, given a foreground image and a background image, our proposed method automatically generates a set of blending photos with scores that indicate the aesthetics quality with the proposed quality network and policy network. Experimental results show that the proposed approach can effectively generate high quality blending photos with efficiency.

AB - Photo blending is a common technique to create aesthetically pleasing artworks by combining multiple photos. However, the process of photo blending is usually time-consuming, and care must be taken in the process of blending, filtering, positioning, and masking each of the source photos. To make photo blending accessible to general public, we propose an efficient approach for automatic photo blending via deep learning. Specifically, given a foreground image and a background image, our proposed method automatically generates a set of blending photos with scores that indicate the aesthetics quality with the proposed quality network and policy network. Experimental results show that the proposed approach can effectively generate high quality blending photos with efficiency.

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Hung WC, Zhang J, Shen X, Lin Z, Lee JY, Yang MH. Learning to blend photos. In Ferrari V, Sminchisescu C, Hebert M, Weiss Y, editors, Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. Springer Verlag. 2018. p. 72-88. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-01234-2_5