Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

May 1, 2004, Vol. 16, No. 5, Pages 1039-1062
(doi: 10.1162/089976604773135096)
© 2004 Massachusetts Institute of Technology
Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning
Article PDF (962.38 KB)
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.