Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning
Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning
复制标题
DOI：
10.1162/089976604773135096
复制
发表时间：
2004-05
影响因子：
2.9
通讯作者：
Christopher K. I. Williams;Michalis K. Titsias
中科院分区：
文献类型：
--
作者：
Christopher K. I. Williams;Michalis K. Titsias
我们考虑的数据是包含多个对象视图的图像。我们的任务是了解图像中存在的每个对象。该任务可以作为阶乘学习问题来处理，其中必须通过为具有正确实例化参数的每个对象实例化模型来解释每个图像。学习阶乘模型的一个主要问题是，随着对象数量的增加，需要考虑的配置数量会出现组合爆炸。我们开发了一种利用稳健的统计方法从数据中顺序提取对象模型的方法，从而避免了组合爆炸，并给出了从真实图像中成功提取对象的结果。
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.