Photographic composition classification and dominant geometric element detection for outdoor scenes

Jun Tae Lee, Han Ul Kim, Chul Lee, Chang-Su Kim

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Despite the practical importance of photographic composition for improving or assessing the aesthetical quality of photographs, only a few simple composition rules have been considered for its classification. In this work, we propose novel techniques to classify photographic composition rules of outdoor scenes and detect dominant geometric elements, called composition elements, for each composition class. Specifically, we first categorize composition rules of outdoor photographs into nine classes: RoT, center, horizontal, symmetric, diagonal, curved, vertical, triangle, and pattern. Then, we develop a photographic composition classification algorithm using a convolutional neural network (CNN). To train the CNN, we construct a photographic composition database, which is publicly available. Finally, for each composition class, we propose an effective scheme to locate composition elements, i.e., bounding boxes for main subjects, leading lines, axes of symmetry, triangles, and sky regions. Extensive experimental results demonstrate that the proposed algorithm classifies composition classes reliably and detects composition elements accurately.

Original languageEnglish
Pages (from-to)91-105
Number of pages15
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - 2018 Aug


  • Composition element detection
  • Geometric element detection
  • Image classification
  • Photographic composition
  • Rule of thirds
  • Sky detection

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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