Unsupervised learning for hierarchical image modelling

It is highly desirable to frame image understanding in terms of hierarchical generative probabilistic models. These allow top-down and bottom-up flows of information to take place, in order to provide a scene interpretation. Encoded within such a model would be knowledge at various levels, e.g. lower-level models of regions and boundaries, and at a higher level the shape and appearance of object classes, and their contextual relationships. Due to the difficulties in obtaining appropriate annotated data, such models should be learned in a largely unsupervised fashion from image data. Hinton's "deep learning" agenda is attractive here in that it provides an upgrade path from lower-level to higher-level regularities.

The specific PhD project would develop components that would fit into this framework; for example one might decompose an image into regions based on visual texture, and at a higher level model the typical shapes and appearances of co-occurring regions that arise from object classes.

This is an opportunity to join a world-leading research group in machine learning. The machine learning group in the Institute for Adaptive and Neural Computation is made up of 6 academic staff: Chris Bishop, Chris Williams, Amos Storkey, Charles Sutton, Guido Sanguinetti, Iain Murray. The group conducts research into the development of novel probabilistic machine learning methods, and the application of these novel methods to cutting edge scientific/technological problems.

The project is suitable for a student with a strong mathematical background, ideally a first class degree in Computer Science, Mathematics, Physics or Engineering. Some knowledge of machine learning and computer vision is highly desirable. Informal enquiries should be addressed to Prof Chris Williams; please attach a CV, a list of courses taken and grades obtained, and a 1-2 page statement of your research interests. See http://homepages.inf.ed.ac.uk/ckiw/mypages/students.html for more information for prospective PhD students. We recommend that students make informal contact a before making a formal application, ideally as soon as possible: the more time between informal contact and the application deadline the better. Application deadlines match the standard Informatics deadlines at http://www.ed.ac.uk/schools-departments/informatics/postgraduate/apply/keydatesresearchappns.

We advise students to apply before the 16 December 2011 deadline.

Formal application must be through the School's normal PhD application process: http://www.ed.ac.uk/schools-departments/informatics/postgraduate/apply Select the Informatics: Institute for Adaptive and Neural Computation research area.