TY  - CONF
ID  - SisLab3790
UR  - http://dx.doi.org/10.1145/3240508.3240711
A1  - Nguyen, Diep Thi Ngoc
A1  - Nakayama, Hideki
A1  - Okazaki, Naoaki
A1  - Sakaeda, Tatsuya
Y1  - 2018///
N2  - Aiming to develop of computational grammar system for visual information, we design a 4-tier framework that consists of four levels of 'visual grammar of images.' As a first step of realization, we propose a new dataset, named the PoB dataset, in which each image is annotated with multiple labels of armature patterns that compose the pictorial scene. The PoB dataset includes of a 10,000-painting dataset for art and a 4,959-image dataset for photography. In this paper, we discuss the consistency analysis of our dataset and its applicability. We also demonstrate how the armature patterns in the PoB dataset are useful in assessing aesthetic quality of images, and how well a deep learning algorithm can recognize these patterns. This paper seeks to set a new direction in image understanding with a more holistic approach beyond discrete objects and in aesthetic reasoning with a more interpretative way.
TI  - PoB: Toward Reasoning Patterns of Beauty in Image Data
SP  - 1786
M2  - Seoul, Republic of Korea
AV  - public
EP  - 1793
T2  - The 26th ACM international conference on Multimedia
ER  -