People-LDA: Anchoring Topics to People using Face Recognition

Topic models have recently emerged as powerful tools for modeling topical trends in documents. Often the resulting topics are broad and generic, associating large groups of people and issues that are loosely related. In many cases, it may be desirable to influence the direction in which topic models develop. In this paper, we explore the idea of centering topics around people. In particular, given a large corpus of images featuring collections of people and associated captions, it seems natural to extract topics specifically focussed on each person. What words are most associated with George Bush? Which with Condoleezza Rice? Since people play such an important role in life, it is natural to anchor one topic to each person. In this paper, we present People-LDA, which uses the coherence of face images in news captions to guide the development of topics. In particular, we show how topics can be refined to be more closely related to a single person (like George Bush) rather than describi...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Vidit Jain, Erik G. Learned-Miller, Andrew McCallum
• Keywords: Computer Vision, Face Images, ICCV 2007, Images Featuring Collections, Modern Face Recognizer, Refined Topics, Topic Models