Predicting Professions through Probabilistic Model under Social Context

Ming Shao, Liangyue Li, Yun Fu

Abstract: In this paper, we investigate the problem of predicting people's professions under social context. Previous work considering clothing information as well as fore/background context preliminarily proves the feasibility of predicting professions. In this paper, we discuss this problem in a more general case -- multiple people in one photo with arbitrary poses, and argue that with appropriately built partial body features, spatial relations, and background context, more appealing results are achieved by a probabilistic model. We conduct experiments on 14 representative professions with over 7000 images, and demonstrate the model's superiority with impressive results.