Variable-Length Hashing
This addresses storage and search efficiency for large-scale similarity search, but appears incremental as it builds on existing hashing techniques.
The paper tackles the problem of storage inefficiency in learning-based hashing methods by proposing a lossless variable-length hashing (VLH) method that improves retrieval performance with little sacrifice in storage or search complexity, achieving significantly improved retrieval performance with no increase in storage and marginal increase in computational cost.
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.