稀疏非局部 CRF 及其应用

Olga Veksler, Yuri Boykov
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引用次数: 0

摘要

CRF 为经典和深度学习计算机视觉中的空间一致性建模。最常见的 CRF 被称为成对 CRF,因为它连接像素对。成对 CRF 有两种类型:稀疏型和密集型。稀疏 CRF 连接的是附近的像素,因此在图像大小上连接的数量是线性的。密集 CRF 连接所有像素对,连接数为二次方。虽然密集 CRF 是一种更通用的模型,但其效率远低于稀疏 CRF。事实上,只有高斯边缘稠密 CRF 在实践中被使用,即使如此,也是近似值。我们提出了一种新的成对 CRF,称之为稀疏非局部 CRF。与密集 CRF 一样,它也具有非本地连接,因此比稀疏 CRF 更为通用。与稀疏 CRF 一样,连接数是线性的,因此我们的模型是高效的。除了效率之外,我们的另一个优势是边缘权重不受限制。我们的研究表明,我们的稀疏非局部 CRF 模型具有与高斯密集 CRF 相似的特性。我们还讨论了与其他 CRF 模型的联系。我们证明了我们的模型在经典和深度学习应用中的实用性,适用于两个和多个标签。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sparse Non-Local CRF With Applications.

CRFs model spatial coherence in classical and deep learning computer vision. The most common CRF is called pairwise, as it connects pixel pairs. There are two types of pairwise CRF: sparse and dense. A sparse CRF connects the nearby pixels, leading to a linear number of connections in the image size. A dense CRF connects all pixel pairs, leading to a quadratic number of connections. While dense CRF is a more general model, it is much less efficient than sparse CRF. In fact, only Gaussian edge dense CRF is used in practice, and even then with approximations. We propose a new pairwise CRF, which we call sparse non-local CRF. Like dense CRF, it has non-local connections, and, therefore, it is more general than sparse CRF. Like sparse CRF, the number of connections is linear, and, therefore, our model is efficient. Besides efficiency, another advantage is that our edge weights are unrestricted. We show that our sparse non-local CRF models properties similar to that of Gaussian dense CRF. We also discuss connections to other CRF models. We demonstrate the usefulness of our model on classical and deep learning applications, for two and multiple labels.

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