title: Greedy learning of multiple objects in images using robust statistics and factorial learning.
abstract: We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.
authors.affiliation: School of Informatics, University of Edinburgh, Edinburgh EH1 2QL, UK. c.k.i.williams@ed.ac.uk
authors.forename: Christopher K I
authors.initials: CK
authors.lastname: Williams
authors.forename: Michalis K
authors.initials: MK
authors.lastname: Titsias
mesh_terms: D000465:Algorithms; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D010775:Photic Stimulation / Q000379:methods*