Model Parameter Re-optimization for Linear Hashing Based on Similarity Drive

被引：0
|
作者：
Nie X.-S. [1 ]
Liu X.-B. [2 ,3 ]
Xi X.-M. [1 ]
Yin Y.-L. [3 ]
机构：
[1] School of Computer Science and Technology, Shandong Jianzhu University, Ji'nan
[2] School of Computer Science and Technology, Shandong University, Qingdao
[3] School of Software, Shandong University, Ji'nan
来源：
Ruan Jian Xue Bao/Journal of Software | 2020年 / 31卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Content retrieval; Hash learning; Linear model; Parameter optimization; Similarity drive;
D O I：
10.13328/j.cnki.jos.005918
中图分类号：
学科分类号：
摘要：
By designing and optimizing an objective function, and combining the distribution of samples, hash learning learns the hash codes of samples. In the existing hashing models, linear model is widely used due to its conciseness and high efficiency. For the parameter optimization of linear hashing model, a model parameter re-optimization method is propose based on similarity drive, which can improve the precision of the existing linear model-based hashing algorithms. Given a hashing method, this method is firstly run for several times with obtaining several hash matrices. Then, some bits are selected for these hash matrices to obtain a new final hash matrix based on the similarity preserving degree and a fusion strategy. Finally, this new hash matrix is used to re-optimize the model parameters, and a better hash model is obtained for out-of-sample extension. Extensive experiments are performed based on three benchmark datasets and the results demonstrate the superior performance of the proposed framework. © Copyright 2020, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：1039 / 1050
页数：11
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