Title: Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks,
Abstract: An image is a very effective tool for conveying emotions. Many researchers
have investigated in computing the image emotions by using various features
extracted from images. In this paper, we focus on two high level features, the
object and the background, and assume that the semantic information of images
is a good cue for predicting emotion. An object is one of the most important
elements that define an image, and we find out through experiments that there
is a high correlation between the object and the emotion in images. Even with
the same object, there may be slight difference in emotion due to different
backgrounds, and we use the semantic information of the background to improve
the prediction performance. By combining the different levels of features, we
build an emotion based feed forward deep neural network which produces the
emotion values of a given image. The output emotion values in our framework are
continuous values in the 2-dimensional space (Valence and Arousal), which are
more effective than using a few number of emotion categories in describing
emotions. Experiments confirm the effectiveness of our network in predicting
the emotion of images.