使用转换的Indian Buffet过程建模图像
Modeling Images using Transformed Indian Buffet Processes
摘要：
潜在特征模型对于图像建模是有吸引力的，因为图像通常包含多个对象。然而，许多潜在特征模型忽略了对象可能出现在不同的位置或需要对图像进行预分割。虽然变换的印度自助过程（tIBP）提供了一种方法，用于在未分割的二进制图像中建模变换不变的功能，其目前的形式是不适合真实的图像，因为它的计算成本和建模假设。我们将tIBP与适合真实图像的似然性相结合，并使用图像和特征之间的互相关来开发一种有效的推理，在理论上和经验上都比现有的推理技术更快。我们的方法发现合理的组成部分，实现有效的图像重建自然图像。
英文摘要：
Latent feature models are attractive for image modeling, since images generally contain multiple objects. However, many latent feature models ignore that objects can appear at different locations or require pre-segmentation of images. While the transformed Indian buffet process (tIBP) provides a method for modeling transformation-invariant features in unsegmented binary images, its current form is inappropriate for real images because of its computational cost and modeling assumptions. We combine the tIBP with likelihoods appropriate for real images and develop an efficient inference, using the cross-correlation between images and features, that is theoretically and empirically faster than existing inference techniques. Our method discovers reasonable components and achieve effective image reconstruction in natural images.
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