Pose-Based Composition Improvement for Portrait Photographs
| Autor: | Zhenhua Tang, Kap Luk Chan, Xiaoyan Zhang, Zhuopeng Li, Gaoyang Tang, Martin Constable |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Painting
Similarity (geometry) Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology GeneralLiterature_MISCELLANEOUS Portrait Face (geometry) 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Composition (visual arts) Computer vision Artificial intelligence Electrical and Electronic Engineering business Pose Portrait painting ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 29:653-668 |
| ISSN: | 1558-2205 1051-8215 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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