Emotions in abstract art: Does texture matter?

Andreza Sartori*, Berhan Şenyazar, Almila Akdag, Albert Ali Salah, Nicu Sebe

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The classification of images based on the emotions they evoke is a recent approach in multimedia. With the abundance of digitized images from museum archives and the ever-growing digital production of user-generated images, there is a greater need for intelligent image retrieval algorithms. Categorization of images according to their emotional impact offers a useful addition to the state of the art in image search. In this work, we apply computer vision techniques on abstract paintings to automatically predict emotional valence based on texture. We also propose a method to derive a small set of features (Perlin parameters) from an image to represent its overall texture. Finally, we investigate the saliency distribution in these images, and show that computational models of bottom-up attention can be used to predict emotional valence in a parsimonious manner.

Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2015 - 18th International Conference, Proceedings
EditorsVittorio Murino, Enrico Puppo, Vittorio Murino
PublisherSpringer
Pages671-682
Number of pages12
ISBN (Print)9783319232300
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event18th International Conference on Image Analysis and Processing, ICIAP 2015 - Genoa, Italy
Duration: 7 Sept 201511 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9279
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Image Analysis and Processing, ICIAP 2015
Country/TerritoryItaly
CityGenoa
Period7/09/1511/09/15

Keywords

  • Abstract paintings
  • Emotion recognition
  • Eye-tracking
  • Perlin images
  • Saliency

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