Property-Specific Aesthetic Assessment With Unsupervised Aesthetic Property Discovery
- Authors
- Lee, Jun-Tae; Lee, Chul; Kim, Chang-Su
- Issue Date
- 2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Image aesthetics; aesthetic assessment; image composition; convolutional neural network; unsupervised property discovery; and unsupervised attribute clustering
- Citation
- IEEE ACCESS, v.7, pp.114349 - 114362
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 7
- Start Page
- 114349
- End Page
- 114362
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/68901
- DOI
- 10.1109/ACCESS.2019.2936289
- ISSN
- 2169-3536
- Abstract
- We propose the property-specific aesthetic assessment (PSAA) algorithm with unsupervised aesthetic property discovery. The proposed PSAA algorithm uses an aesthetic feature extractor, an aesthetic property classifier, and multiple property-specific assessment networks. The aesthetic feature extractor analyzes aesthetics of images to generate features. Using such aesthetic features, we discover diverse aesthetic properties in an unsupervised manner and develop the aesthetic property classifier to predict the aesthetic property of each image. For each discovered aesthetic property, we train a property-specific assessment network. Thus, we can assess the aesthetic quality of an image using the property-specific network that corresponds to its property. Experimental results on a large dataset show that the proposed PSAA algorithm achieves state-of-the-art aesthetic assessment performance. Furthermore, we demonstrate that PSAA is useful for improving aesthetic qualities of images in two applications: contrast enhancement and image cropping.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.