1 research outputs found
Reinforcing Short-Length Hashing
Due to the compelling efficiency in retrieval and storage,
similarity-preserving hashing has been widely applied to approximate nearest
neighbor search in large-scale image retrieval. However, existing methods have
poor performance in retrieval using an extremely short-length hash code due to
weak ability of classification and poor distribution of hash bit. To address
this issue, in this study, we propose a novel reinforcing short-length hashing
(RSLH). In this proposed RSLH, mutual reconstruction between the hash
representation and semantic labels is performed to preserve the semantic
information. Furthermore, to enhance the accuracy of hash representation, a
pairwise similarity matrix is designed to make a balance between accuracy and
training expenditure on memory. In addition, a parameter boosting strategy is
integrated to reinforce the precision with hash bits fusion. Extensive
experiments on three large-scale image benchmarks demonstrate the superior
performance of RSLH under various short-length hashing scenarios