- Abstract:
-
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 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.
- Links To Paper
- 1st link
- Bibtex format
- @Article{EDI-INF-RR-0259,
- author = {
Chris Williams
and Michalis Titsias
},
- title = {Greedy Learning of Multiple Objects in Images using Robust Statistics and Factorial Learning},
- journal = {Neural Computation},
- publisher = {MIT Press},
- year = 2004,
- month = {May},
- volume = {16(5)},
- pages = {1039-1062},
- doi = {10.1162/089976604773135096},
- url = {http://homepages.inf.ed.ac.uk/ckiw/papers/lmo8.pdf},
- }
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