An affect-based classification of emotions associated with images of food

Food and emotions are correlated. Recent research on the relationship between foods and emotions mainly focused on identifying emotions when viewing food images. The studies try to find image attributes that evoke food-related emotions. We concentrate on affective image classification and investigate the performance of different features in a food-related emotion classification framework. First, we extract features of different levels for each food image. Very basic low-level features and art features derived from principle-of-art features are extracted as mid-level features. Then, we develop models for valence-arousal affect dimensions trained using different machine learning techniques. Extensive experiments are conducted on a combined food image dataset. The results demonstrate the effectiveness of the proposed food-related emotion classification method. The results demonstrate the effectiveness of the proposed food-related emotion classification model by comparing different classifiers for the two affect dimensions (valence and arousal), resulting in an accuracy of 67% and 88% respectively.