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A Compression Hashing Scheme for Large-Scale Face Retrieval

机译：大型面部检索的压缩散列方案

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Hashing method has the intrinsic problem that a long binary code yields better precision but requires a larger storage cost. Most of existing hashing methods aim to find an optimal code length to trade off the precision and storage. However, in reality, the scale of the face images is enormous and thus the storage burden is unimaginative heavy. We propose to apply a similarity-preserving compression scheme to existing unsupervised hashing methods, so as to reduce storage burden while maintaining a high precision. We employ two different lengths of code, including a long code with original length and a short code with length after m-time compression. The hash code for the query face preserves the original code length while the hash code for stored image is compressed with a ratio m to reduce storage cost. When performing face retrieval, the compressed hash code for the stored face is m-time repeatedly concentrated, in order to be compared with the long hash code for the query based on Hamming distance. Experimental results on large-scale retrieval demonstrate that the proposed compression scheme can be efficiently applied in existing methods and achieves both a high precision and a small storage space.
机译：散列方法具有长二进制代码产生更好的精度但需要更大的存储成本。大多数现有散列方法旨在找到最佳的代码长度来折衷精度和存储。然而，实际上，面部图像的规模是巨大的，因此储存负担是难以想象的重。我们建议将相似性保存的压缩方案应用于现有无监督的散列方法，以减少存储负担，同时保持高精度。我们使用两个不同的代码长度，包括一个具有原始长度的长代码和M-Time压缩后长度的短代码。查询面部的哈希代码保留原始代码长度，而存储图像的散列码被压缩，则以比率m压缩，以降低存储成本。在执行面部检索时，存储面的压缩散列码是重复集中的m-time，以便与基于汉明距离的查询的长哈希码进行比较。大规模检索的实验结果表明，所提出的压缩方案可以有效地应用现有方法，并实现高精度和小存储空间。

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