Interactive optimization of photo composition with Gaussian mixture model on mobile platform

Hachon Sung, Guntae Bae, Sunyoung Cho, Hyeran Byun

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


A good photo is determined using various visual elements of photography and these elements have been implemented in mobile devices with functionalities including zooming, auto-focusing and auto-white-balancing. Although composition is an important element of a good photo and an interesting research topic, most composition-related functionalities have not been added to mobile devices. We propose a guide system for capturing good photos in mobile devices that considers composition elements. A photo composition mixture model (PCMM) is derived based on composition elements such as a Gaussian Mixture Model (GMM), and the best composition of current input is gradually determined by iterating the PCMM optimization. Experimental evaluations are conducted to show the usefulness of the proposed PCMM and its optimization performance. To show the efficiency of recomposition performance and speed, we compare our method with retargeting-based methods. By implementing our method in mobile devices, we show that our system offers valid user guidance for capturing a photo with good composition in realtime.

Original languageEnglish
Article number017001
JournalOptical Engineering
Issue number1
Publication statusPublished - 2012 Jan

Bibliographical note

Funding Information:
This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2011-(C1090-1121-0008)).

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)


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