Back to articles
JIST-first
Volume: 30 | Article ID: art00037
Image
Saliency-Based Artistic Abstraction With Deep Learning and Regression Trees
  DOI :  10.2352/J.ImagingSci.Technol.2017.61.6.060402  Published OnlineNovember 2017
Abstract

Abstraction in art often reflects human perception—areas of an artwork that hold the observer's gaze longest will generally be more detailed, while peripheral areas are abstracted, just as they are mentally abstracted by humans' physiological visual process. The authors' artistic abstraction tool, Salience Stylize, uses Deep Learning to predict the areas in an image that the observer's gaze will be drawn to, which informs the system about which areas to keep the most detail in and which to abstract most. The planar abstraction is done by a Random Forest Regressor, splitting the image into large planes and adding more detailed planes as it progresses, just as an artist starts with tonally limited masses and iterates to add fine details, then completed with our stroke engine. The authors evaluated the aesthetic appeal and effectiveness of the detail placement in the artwork produced by Salience Stylize through two user studies with 30 subjects.

Subject Areas :
Views 13
Downloads 7
 articleview.views 13
 articleview.downloads 7
  Cite this article 

Hanieh Shakeri, Michael Nixon, Steve DiPaola, "Saliency-Based Artistic Abstraction With Deep Learning and Regression Treesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging,  2017,  pp 1 - 9,  https://doi.org/10.2352/J.ImagingSci.Technol.2017.61.6.060402

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2018
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology