Locality preserving hashing for fast image search: theory and applications

Is there any Java library that provides an implementation (or several) of a Locality Preserving Hash Function for Strings?

有没有Java类库提供Locality Perserving Hash方法的实现？

ABSTRACT摘要
State-of-the-art hashing methods, such as the kernelised locality-sensitive hashing and spectral hashing, have high algorithmic complexities to build the hash codes and tables. Our observation from the existing hashing method is that, putting two dissimilar data points into the same hash bucket only reduces the efficiency of the hash table, but it does not hurt the query accuracy. Whereas putting two similar data points into different hash buckets will reduce the correctness (i.e. query accuracy) of a hashing method. Therefore, it is much more important for a good hashing method to ensure that similar data points have high probabilities to be put to the same bucket, than considering those dissimilar data-point relations. On the other side, attracting similar data points to the same hash bucket will naturally suppress dissimilar data points to be put into the same hash bucket. With this locality-preserving observation, we naturally propose a new hashing method called the locality-preserving hashing, which builds the hash codes and tables with much lower algorithmic complexity. Experimental results show that the proposed method is very competitive in terms of the training time spent for large data-sets among the state of the arts, and with reasonable
or even better query accuracy.

现有的哈希方法，如核化的局部敏感哈希和谱哈希，在建立哈希码和表时具有很高的算法复杂度。我们从现有的哈希方法中观察到，将两个不同的数据点放入相同的哈希桶中只会降低哈希表的效率，但不会影响查询精度。但是，将两个相似的数据点放入不同的哈希捅将降低该哈希方法的的准确性（例如，查询精度）。因此，与其考虑这些不相似的数据点关系相比，一个好的哈希方法更重要地是要确保相似数据点具有高的概率被放到相同的桶中。从另一方面来说，将相似的数据点吸引到相同的哈希桶也会自然地抑制不相关的数据点被放入相同的哈希桶。使用这种locality-preserving的观察方法，我们自然地提出了一种新的哈希方法叫locality-preserving hashing，它在建立哈希码和哈希表时使用的是更低的算法复杂度。实验结果表明，所提出的方法在训练大数据集的时间上具有很强的竞争力，并且是合理的甚至更好的查询精度。