Modeling Photo Composition and Its Application to Photo Re-arrangement

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 412
  • Download : 48
We introduce a learning based photo composition model and its application on photo re-arrangement. In contrast to previous approaches which evaluate quality of photo composition using the rule of thirds or the golden ratio, we train a normalized saliency map from visually pleasurable photos taken by professional photographers. We use Principal Component Analysis (PCA) to analyze training data and build a Gaussian mixture model (GMM) to describe the photo composition model. Our experimental results show that our approach is reliable and our trained photo composition model can be used to improve photo quality through photo re-arrangement.
Publisher
IEEE
Issue Date
2012-10
Language
English
Citation

IEEE International Conference on Image Processing

URI
http://hdl.handle.net/10203/173195
Appears in Collection
EE-Conference Papers(학술회의논문)

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0