Improving People Search Using Query Expansions

In this paper we are interested in finding images of people on the web, and more specifically within large databases of captioned news images. It has recently been shown that visual analysis of the faces in images returned on a text-based query over captions can significantly improve search results. The underlying idea to improve the text-based results is that although this initial result is imperfect, it will render the queried person to be relatively frequent as compared to other people, so we can search for a large group of highly similar faces. The performance of such methods depends strongly on this assumption: for people whose face appears in less than about 40% of the initial text-based result, the performance may be very poor. The contribution of this paper is to improve search results by exploiting faces of other people that co-occur frequently with the queried person. We refer to this process as `query expansion'. In the face analysis we use the query expansion to provid...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Thomas Mensink, Jakob J. Verbeek
• Keywords: Computer Vision, ECCV 2008, Gaussian Mixture Model, Logistic Discriminant Model, Query Expansion, Query-specific Relevant Set, Text-based Query