Abstract:
To make machine emotionally intelligent and to have a ective decision making, emotion is
one of the basic human attribute which is used in a ective classi cation . In this research
work food images are used to evoke emotions by using principle of art features(emphasis,
gradation, variety, symmetry, movement and harmony). These features are extracted from
the images to form a feature vector and as a result a food-emotion model is created.
Emotional labels to these images are associated with help of valence arousal psychological
model. Three classi ers SVM(Support Vector Machine), MLP (Multi-layer Perceptron )
and Naive Bayes are used to form a classi cation model. This classi cation approach
help in forming a 4-label and 3-label model which associate emotional attribute Happy,
Relax,Stress and Boring in the food images. With the help of these results decision making
become more related to human attribute of emotion. Among all models, 3-label model give
accuracy of 58%.
