Pose-based Composition Improvement for Portrait Photographs

Zhang, X, Chan, K and Constable, M 2018, 'Pose-based Composition Improvement for Portrait Photographs', in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orleans, United States, 5-9 March 2017, pp. 1962-1966.


Document type: Conference Paper
Collection: Conference Papers

Title Pose-based Composition Improvement for Portrait Photographs
Author(s) Zhang, X
Chan, K
Constable, M
Year 2018
Conference name ICASSP 2017
Conference location New Orleans, United States
Conference dates 5-9 March 2017
Proceedings title Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017)
Publisher IEEE
Place of publication United States
Start page 1962
End page 1966
Total pages 5
Abstract This paper studies the composition in portrait paintings and develops an algorithm to improve the composition of portrait photographs based on example portrait paintings. A study of portrait paintings shows that the placement of the face and the figure is pose-related. Based on this observation, this paper develops an algorithm to improve the composition of a portrait photograph by learning the placement of the face and the figure from an example portrait painting. This example portrait painting is selected based on the similarity of its figure pose to that of the input photograph. This similarity measure is modeled as a graph matching problem. Finally, space cropping is performed using an optimization function to assign a similar location for each body part of the figure in the photograph with that of the figure in the example portrait painting. The experimental results demonstrate the effectiveness of the proposed method. A user study shows that the proposed pose-based composition improvement is preferred more than rule-based methods and learning-based methods.
Subjects Computer Vision
Fine Arts (incl. Sculpture and Painting)
Keyword(s) Painting
Computational Aesthetics
DOI - identifier 10.1109/ICASSP.2017.7952499
Copyright notice © 2017 IEEE
ISBN 9781509041176
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