Where do emotions come from? Predicting the Emotion Stimuli Map

Kuan Chuan Peng, Amir Sadovnik, Andrew Gallagher, Tsuhan Chen

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

90 Scopus citations

Abstract

Which parts of an image evoke emotions in an observer? To answer this question, we introduce a novel problem in computer vision - predicting an Emotion Stimuli Map (ESM), which describes pixel-wise contribution to evoked emotions. Building a new image database, EmotionROI, as a benchmark for predicting the ESM, we find that the regions selected by saliency and objectness detection do not correctly predict the image regions which evoke emotion. Although objects represent important regions for evoking emotion, parts of the background are also important. Based on this fact, we propose using fully convolutional networks for predicting the ESM. Both qualitative and quantitative experimental results confirm that our method can predict the regions which evoke emotion better than both saliency and objectness detection.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages614-618
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Externally publishedYes
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

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

Conference

Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period09/25/1609/28/16

Keywords

  • Emotion stimuli map
  • Fully convolutional networks

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