Efficient occlusive components analysis
Efficient occlusive components analysis
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DOI：
10.5555/2627435.2697053
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Henniges;Richard E. Turner;M. Sahani;J. Eggert;Jörg Lücke
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文献类型：
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作者：
M. Henniges;Richard E. Turner;M. Sahani;J. Eggert;Jörg Lücke
我们在遮挡的概率生成模型中研究无监督学习。该模型使用两种类型的潜在变量：一种指示图像中存在哪些对象，另一种指示它们在深度上的排序方式。然后，该深度顺序确定模型参数中指定的存在对象的位置和外观如何组合以形成图像。我们表明，可以从一组未标记的图像中学习对象参数，其中对象彼此遮挡。精确的最大似然学习是很棘手的。然而，通过将截断变分方法应用于期望最大化（EM），可以导出易于处理的近似值。在数值实验中表明，这些近似可以恢复底层的对象参数集，包括数据噪声和稀疏性。使用彩色条对条形测试的新颖版本进行的实验以及对更真实数据的实验表明，该算法在提取生成组件方面表现良好。研究的方法表明，多原因生成方法可以推广到提取遮挡成分，这将遮挡研究与稀疏编码方法领域联系起来。
We study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learned from an unlabeled set of images in which objects occlude one another. Exact maximum-likelihood learning is intractable. Tractable approximations can be derived, however, by applying a truncated variational approach to Expectation Maximization (EM). In numerical experiments it is shown that these approximations recover the underlying set of object parameters including data noise and sparsity. Experiments on a novel version of the bars test using colored bars, and experiments on more realistic data, show that the algorithm performs well in extracting the generating components. The studied approach demonstrates that the multiple-causes generative approach can be generalized to extract occluding components, which links research on occlusion to the field of sparse coding approaches.