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Photo Sequences of Varying Emotion: Optimization with a Valence-Arousal Annotated Dataset

Photo Sequences of Varying Emotion: Optimization with a Valence-Arousal Annotated Dataset Synthesizing photo products such as photo strips and slideshows using a database of images is a time-consuming and tedious process that requires significant manual work. To overcome this limitation, we developed a method that automatically synthesizes photo sequences based on several design parameters. Our method considers the valence and arousal ratings of images in conjunction with parameters related to both the visual consistency of the synthesized photo sequence and the progression of valence and arousal throughout the photo sequence. Our method encodes valence, arousal, and visual consistency parameters as cost terms into a total cost function while applying a Markov chain Monte Carlo optimization techniques called simulated annealing to synthesize the photo sequence based on user-defined target objectives in a few seconds. As our method was developed for the synthesis of photo sequences using the valence-arousal emotional model, a user study was conducted to evaluate the efficacy of the synthesized photo sequences in triggering valence-arousal ratings as expected. Our results indicate that the proposed method synthesizes photo sequences in which valence and arousal dimensions are perceived as expected by participants; however, valence may be more appropriately perceived than arousal. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Photo Sequences of Varying Emotion: Optimization with a Valence-Arousal Annotated Dataset

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2021 Association for Computing Machinery.
ISSN
2160-6455
eISSN
2160-6463
DOI
10.1145/3458844
Publisher site
See Article on Publisher Site

Abstract

Synthesizing photo products such as photo strips and slideshows using a database of images is a time-consuming and tedious process that requires significant manual work. To overcome this limitation, we developed a method that automatically synthesizes photo sequences based on several design parameters. Our method considers the valence and arousal ratings of images in conjunction with parameters related to both the visual consistency of the synthesized photo sequence and the progression of valence and arousal throughout the photo sequence. Our method encodes valence, arousal, and visual consistency parameters as cost terms into a total cost function while applying a Markov chain Monte Carlo optimization techniques called simulated annealing to synthesize the photo sequence based on user-defined target objectives in a few seconds. As our method was developed for the synthesis of photo sequences using the valence-arousal emotional model, a user study was conducted to evaluate the efficacy of the synthesized photo sequences in triggering valence-arousal ratings as expected. Our results indicate that the proposed method synthesizes photo sequences in which valence and arousal dimensions are perceived as expected by participants; however, valence may be more appropriately perceived than arousal.

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Jul 21, 2021

Keywords: Valence

References