Spatial distance dependent Chinese restaurant processes for image segmentation
Spatial distance dependent Chinese restaurant processes for image segmentation
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发表时间：
2011-12
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通讯作者：
S. Ghosh;Andrei Ungureanu;Erik B. Sudderth;D. Blei
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作者：
S. Ghosh;Andrei Ungureanu;Erik B. Sudderth;D. Blei
距离相关的中餐馆过程(DdCRP)最近被引入以适应不可交换数据的随机分区[1]。Dd-crp以一种有偏见的方式对数据进行集群：每个数据点更有可能与外部意义上靠近它的其他数据集群。本文以自然图像分割为目标，研究了空间背景下的dd-CRP。我们研究了空间ddCRP模型的偏差，并提出了一种新的层次扩展，更适合于产生类似于人类的分割。然后，我们研究了模型对不同距离和外观超参数的敏感性，并首次在图像分割领域提供了非参数贝叶斯模型的严格比较。在非监督图像分割方面，我们证明了使用非常简单的模型和算法可以获得与现有非参数贝叶斯模型类似的性能。
The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of non-exchangeable data [1]. The dd-CRP clusters data in a biased way: each data point is more likely to be clustered with other data that are near it in an external sense. This paper examines the dd-CRP in a spatial setting with the goal of natural image segmentation. We explore the biases of the spatial ddCRP model and propose a novel hierarchical extension better suited for producing "human-like" segmentations. We then study the sensitivity of the models to various distance and appearance hyperparameters, and provide the first rigorous comparison of nonparametric Bayesian models in the image segmentation domain. On unsupervised image segmentation, we demonstrate that similar performance to existing nonparametric Bayesian models is possible with substantially simpler models and algorithms.