Property-specific aesthetic assessment with unsupervised aesthetic property discovery

Jun Tae Lee, Chul Lee, Chang Su Kim

Research output: Contribution to journalArticle

1 Citation (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

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Fingerprint Dive into the research topics of 'Property-specific aesthetic assessment with unsupervised aesthetic property discovery'. Together they form a unique fingerprint.

  • Cite this