TY - CHAP
T1 - New perspectives for the deep learning based photography aesthetics assessment
AU - Asuncion, Vernon
AU - Zhang, Yan
PY - 2025
Y1 - 2025
N2 - Image aesthetics assessment (IAA) has been an important topic in computer vision research. In recent years, many deep learning based approaches have been developed for machine automatic image (photograph) assessment. In this paper, we study the IAA for photography assessment, which captures the most significant aesthetic features in photography. In particular, we re-examine the multi-column deep convolutional neural network architecture, in which photographs are assessed based on their global and local views to make the assessment more precise. Our experiments are conducted with a completely new dataset we have built - Curated Photography Dataset (CPD) which contains over 500,000 photographs crossing eight different categories, and all of these photos have been curated by professional photographers and curators. We show that our approach outperforms the state of the art approaches in the area, and sheds a new light for developing practical AI photography curators in real world domains.
AB - Image aesthetics assessment (IAA) has been an important topic in computer vision research. In recent years, many deep learning based approaches have been developed for machine automatic image (photograph) assessment. In this paper, we study the IAA for photography assessment, which captures the most significant aesthetic features in photography. In particular, we re-examine the multi-column deep convolutional neural network architecture, in which photographs are assessed based on their global and local views to make the assessment more precise. Our experiments are conducted with a completely new dataset we have built - Curated Photography Dataset (CPD) which contains over 500,000 photographs crossing eight different categories, and all of these photos have been curated by professional photographers and curators. We show that our approach outperforms the state of the art approaches in the area, and sheds a new light for developing practical AI photography curators in real world domains.
KW - Classification
KW - Convolutional Neural Networks
KW - Image Aesthetics Assessment
KW - Image Classification
KW - Machine Learning
UR - https://www.scopus.com/pages/publications/85210882842
UR - https://go.openathens.net/redirector/westernsydney.edu.au?url=https://doi.org/10.1007/978-981-96-0351-0_19
U2 - 10.1007/978-981-96-0351-0_19
DO - 10.1007/978-981-96-0351-0_19
M3 - Chapter
AN - SCOPUS:85210882842
SN - 9789819603503
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 256
EP - 268
BT - AI 2024: Advances in Artificial Intelligence: 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, Melbourne, Vic, Australia, November 25-29, 2024, Proceedings, Part II
A2 - Gong, Mingming
A2 - Song, Yiliao
A2 - Koh, Yun Sing
A2 - Xiang, Wei
A2 - Wang, Derui
PB - Springer
CY - Singapore
ER -