Reinforcing Short-Length Hashing
This addresses the need for efficient similarity-preserving hashing in large-scale image retrieval, though it appears incremental as it builds on existing methods to improve short-length scenarios.
The paper tackled the problem of poor performance in image retrieval using extremely short hash codes by proposing Reinforcing Short-Length Hashing (RSLH), which achieved superior performance on three large-scale benchmarks.
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.