Eye gaze analysis and learning-to-rank to obtain the most preferred result in image inpainting

Mariko Isogawa, Dan Mikami, Kosuke Takahashi, Akira Kojima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

This paper proposes a method that blindly predicts preference order between inpainted images, aiming at selecting the best one from a plurality of results. Image inpainting, which removes unwanted regions and restores them, has attracted recent attention. However, it is known that the inpainting result varies largely with the method used for inpainting and the parameters set. Thus, in a typical use case, users need to manually select the inpainting method and the parameter that yields the best one. This manual selection takes a great deal of time and thus there is a great need for a way to automatically estimate the best result. Although some methods, such as estimating perceptual preference score from image features, have been proposed in recent years, none of them are considered very promising approaches. Our method focuses on the following two points: (1) what we essentially need is a preference order relation rather than an absolute score, and (2) we consider that image features for order estimation can be effectively designed by using actually measured human visual attention. Comparison with other image quality assessment methods shows that our method estimates the preference order with high accuracy.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages3538-3542
Number of pages5
ISBN (Electronic)9781467399616
DOIs
Publication statusPublished - 2016 Aug 3
Externally publishedYes
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 2016 Sep 252016 Sep 28

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period16/9/2516/9/28

Keywords

  • Eye tracking
  • Gaze
  • Image inpainting
  • Image quality assessment (IQA)
  • Learning to rank

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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