Property-specific aesthetic assessment with unsupervised aesthetic property discovery

Jun Tae Lee, Chul Lee, Chang Su Kim

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Article number2936289
Pages (from-to)114349-114362
Number of pages14
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported in part by the Cross-Ministry Giga Korea Project Grant funded by the Korean Government (MSIT) (Development of 4D Reconstruction and Dynamic Deformable Action Model-Based Hyper-Realistic Service Technology) under Grant GK18P0200, and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) under Grant NRF-2018R1A2B3003896.

Publisher Copyright:
© 2020 Royal Society of Chemistry. All rights reserved.

Keywords

  • Aesthetic assessment
  • Convolutional neural network
  • Image aesthetics
  • Image composition
  • Unsupervised attribute clustering
  • Unsupervised property discovery

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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