Composition Improvement for Portrait Photographs
| Autor: | Xiaoyan Zhang, Jinze Yu, Kap Luk Chan, Wang Junyan, Martin Constable |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Painting
Similarity (geometry) business.industry media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data_CODINGANDINFORMATIONTHEORY Art Similarity measure GeneralLiterature_MISCELLANEOUS Portrait Face (geometry) Computer vision Artificial intelligence business Composition (language) Portrait painting ComputingMethodologies_COMPUTERGRAPHICS media_common |
| Zdroj: | Computational Approaches in the Transfer of Aesthetic Values from Paintings to Photographs ISBN: 9789811035593 |
| DOI: | 10.1007/978-981-10-3561-6_8 |
| Popis: | Composition has a great impact upon the visual quality of a photograph. This chapter studies the composition in portrait paintings and proposes an algorithm to improve the composition of portrait photographs based on an example portrait painting. From a study of portrait painting, it can be shown that the placement of the face and the figure in portrait paintings is pose-related. Based on this observation, our algorithm improves the composition of a portrait photograph by referencing the placement of the face and the figure from an example portrait painting. The example portrait painting is selected based on the similarity of its figure pose to that of the input photograph. This similarity measure is modelled as a graph matching problem. Finally, space cropping is performed using an optimisation function to assign a similar location for each body part of the figure in the photograph with that 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 the rule-based methods. |
| Databáze: | OpenAIRE |
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