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Title:Greedy Learning of Multiple Objects in Images using Robust Statistics and Factorial Learning
Authors: Chris Williams ; Michalis Titsias
Date:May 2004
Publication Title:Neural Computation
Publisher:MIT Press
Publication Type:Journal Article Publication Status:Published
Volume No:16(5) Page Nos:1039-1062
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.
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Bibtex format
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 = {},

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