TY - JOUR
T1 - Photographic composition classification and dominant geometric element detection for outdoor scenes
AU - Lee, Jun Tae
AU - Kim, Han Ul
AU - Lee, Chul
AU - Kim, Chang Su
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
KW - Composition element detection
KW - Geometric element detection
KW - Image classification
KW - Photographic composition
KW - Rule of thirds
KW - Sky detection
UR - http://www.scopus.com/inward/record.url?scp=85048460149&partnerID=8YFLogxK
U2 - 10.1016/j.jvcir.2018.05.018
DO - 10.1016/j.jvcir.2018.05.018
M3 - Article
AN - SCOPUS:85048460149
SN - 1047-3203
VL - 55
SP - 91
EP - 105
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
ER -