Learning About Multiple Objects in Images: Factorial Learning without Factorial Search

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Christopher K. I. Williams, Michalis Titsias


We consider data which 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 conīŦgurations 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 avoid- ing the combinatorial explosion, and present results showing successful extraction of objects from real images.