Arten-Net: An Emotion Classification System for Art (Student Consortium)

Art has been part of human culture since time immemorial. It is one of the earliest forms of communication of emotions and stories. Today, people invest a lot of money buying art and there is a need to help in classifying art not only in terms of age or style but also in terms of emotion evoked for ease in locating art displaying similar or same emotions. To the best of our knowledge, no systems exist that utilize multiple modalities for emotion classification of art pieces. This work proposes a classification system called Arten-Net that uses multimodal data from art pieces, title and image, to predict the emotion that art may evoke. Through quantitative and qualitative results we display how an ensemble of multimodal and unimodal classifiers is providing superior results than the multimodal and the unimodal classifiers individually.