Bayesian Models for Massive Multimedia Databases: A New Frontier

Nando de Freitas, Eric Brochu, Kobus Barnard, Pinar Duygulu and David Forsyth
Publishing date
February 18, 2003
12 pages
Modelling the increasing number of digital databases (the web, photo-libraries, music collections, news archives, medical databases) is one of the greatest challenges of statisticians in the new century. Despite the large amounts of data, the models are so large that they motivate the use of Bayesian models. In particular, the Bayesian perspective allows us to perform automatic regularisation to obtain sparse and coherent models. It also enables us to encode a priori knowledge, such as word, music and image preferences. The learned models can be used for browsing digital databases, information retrieval with image, music and/or text queries, image annotation (adding words to an image), text illustration (adding images to a text), and object recognition.