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一致性敏感哈希

Coherency Sensitive Hashing.

出版信息

IEEE Trans Pattern Anal Mach Intell. 2016 Jun;38(6):1099-112. doi: 10.1109/TPAMI.2015.2477814. Epub 2015 Sep 10.

Abstract

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, using 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 and experimented on several extensions, including: k nearest neighbor search, the addition of rotation and matching three dimensional patches in videos.

摘要

一致性敏感哈希 (CSH) 将局部敏感哈希 (LSH) 和 PatchMatch 扩展到能够快速找到两张图像之间匹配的补丁。LSH 依赖于哈希,即将相似的补丁映射到同一个桶中,以找到匹配的补丁。另一方面,PatchMatch 依赖于图像是连贯的这一观察结果,通过在图像平面上的随机补丁分配来传播良好的匹配,从而为初始匹配播种。CSH 依赖于哈希来播种初始补丁匹配,并依赖于图像一致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,映射到同一个桶)。这样,信息的传播速度就会更快,因为它可以利用外观空间或图像平面中的邻域的相似性。因此,CSH 的速度至少比 PatchMatch 快三到四倍,而且更准确,尤其是在纹理区域,因为重建伪影对人眼最明显。我们在一个新的、大规模的 133 对图像数据集上验证了 CSH,并尝试了几个扩展,包括:k 最近邻搜索、旋转的添加以及在视频中匹配三维补丁。

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