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.
Jun-Tae Lee, Han-Ul Kim, Chul Lee, and Chang-Su Kim, "Photographic Composition Classification Photographs and Dominant Geometric Element Detection for Outdoor Scenes," Journal of Visual Communication and Image Representation, Aug. 2018.