  Hashing has emerged as a popular technique for large-scale similarity search.
Most learning-based hashing methods generate compact yet correlated hash codes.
However, this redundancy is storage-inefficient. Hence we propose a lossless
variable-length hashing (VLH) method that is both storage- and
search-efficient. Storage efficiency is achieved by converting the fixed-length
hash code into a variable-length code. Search efficiency is obtained by using a
multiple hash table structure. With VLH, we are able to deliberately add
redundancy into hash codes to improve retrieval performance with little
sacrifice in storage efficiency or search complexity. In particular, we propose
a block K-means hashing (B-KMH) method to obtain significantly improved
retrieval performance with no increase in storage and marginal increase in
computational cost.
