论文标题
加强短长的哈希
Reinforcing Short-Length Hashing
论文作者
论文摘要
由于检索和存储的引人注目的效率，因此相似性保护的散列已被广泛应用于大规模图像检索中的大约最近邻居搜索。但是，由于分类能力较弱和哈希位分布差，因此使用极短的哈希守则在检索中的性能较差。为了解决这个问题，在这项研究中，我们提出了一种新颖的增强短长度的哈希（RSLH）。在拟议的RSLH中，进行哈希表示和语义标签之间的相互重建以保留语义信息。此外，为了提高哈希表示的准确性，成对相似性矩阵旨在在记忆上的准确性和训练支出之间保持平衡。此外，集成了一个参数促进策略，以通过哈希位融合来增强精度。在三个大规模图像基准上进行的广泛实验表明，在各种短长的哈希情景下，RSLH的表现出色。
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.
