Coherency Sensitive Hashing
Coherency Sensitive Hashing
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DOI：
10.1109/iccv.2011.6126421
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Simon Korman;S. Avidan
中科院分区：
文献类型：
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作者：
Simon Korman;S. Avidan
相干敏感散列（CSH）扩展了局部敏感散列（LSH）和PatchMatch，可以快速找到两个图像之间的匹配补丁。LSH依赖于散列，它将相似的补丁映射到相同的bin，以找到匹配的补丁。另一方面，PatchMatch依赖于观察图像是一致的，在图像平面上将良好的匹配传播给相邻的图像。它使用随机补丁分配来播种初始匹配。CSH依赖于散列来播种初始补丁匹配，并依赖于图像相干性来传播良好的匹配。此外，哈希允许它在具有相似外观的补丁之间传播信息（即映射到相同的bin）。通过这种方式，信息的传播速度要快得多，因为它可以利用外观空间中的相似性或图像平面中的邻域。因此，CSH至少比PatchMatch快三到四倍，而且更准确，特别是在纹理区域，重建伪像对人眼来说最明显。我们在133对图像的新的大规模数据集上验证了CSH。
Coherency Sensitive Hashing (CSH) extends Locality Sensitivity Hashing (LSH) and PatchMatch to quickly find matching patches between two images. LSH relies on hashing, which maps similar patches to the same bin, in order to find matching patches. PatchMatch, on the other hand, relies on the observation that images are coherent, to propagate good matches to their neighbors, in the image plane. It uses random patch assignment to seed the initial matching. CSH relies on hashing to seed the initial patch matching and on image coherence to propagate good matches. In addition, hashing lets it propagate information between patches with similar appearance (i.e., map to the same bin). This way, information is propagated much faster because it can use similarity in appearance space or neighborhood in the image plane. As a result, CSH is at least three to four times faster than PatchMatch and more accurate, especially in textured regions, where reconstruction artifacts are most noticeable to the human eye. We verified CSH on a new, large scale, data set of 133 image pairs.