Understanding the emotions behind social images: Inferring with user demographics
Understanding the essential emotions behind social images is of vital importance: it can benefit many applications such as image retrieval and personalized recommendation. While previous related research mostly focuses on the image visual features, in this paper, we aim to tackle this problem by "linking inferring with users' demographics" Specifically, we propose a partially labeled factor graph model named D FGM, to predict the emotions embedded in social images not only by the image visual features, but also by the information of users' demographics. We investigate whether users' demographics like gender, marital status and occupation are related to emotions of social images, and then leverage the uncovered patterns into modeling as different factors. Experiments on a data set from the world's largest image sharing website Flickr1 confirm the accuracy of the proposed model. The effectiveness of the users' demographics factors is also verified by the factor contribution analysis, which reveals some interesting behavioral phenomena as well.
2015 IEEE International Conference on Multimedia and Expo (ICME)