西安电子科技大学学报 ›› 2021, Vol. 48 ›› Issue (3): 71-77.doi: 10.19665/j.issn1001-2400.2021.03.009

• 计算机科学与技术&人工智能 • 上一篇    下一篇

深度共性保持哈希

石娟1(),谢德2(),蒋庆3()   

  1. 1.广西大学 计算机与电子信息学院,广西壮族自治区 南宁 530004
    2.西安电子科技大学 电子工程学院,陕西 西安 710071
    3.百色干部学院 信息化建设与管理部,广西壮族自治区 百色 533013
  • 收稿日期:2019-09-27 出版日期:2021-06-20 发布日期:2021-07-05
  • 通讯作者: 谢德
  • 作者简介:石 娟(1976—),女,讲师,E-mail:sjuan@gxu.edu.cn|蒋 庆(1983—),男,E-mail:33811237@qq.com
  • 基金资助:
    广西重点研发计划(桂科AB18126094)

Deep consistency-preserving hashing

SHI Juan1(),XIE De2(),JIANG Qing3()   

  1. 1. School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
    2. School of Electronic Engineering,Xidian University,Xi’an 710071,China
    3. Dept of Information Construction and Management,Baise Executive Leadership Academy,Baise 533013,China
  • Received:2019-09-27 Online:2021-06-20 Published:2021-07-05
  • Contact: De XIE

摘要:

由于现有的大多数跨模态哈希方法未能有效地探究不同模态数据之间的相关性以及多样性,导致检索性能不尽如人意。为了克服该问题,提出一种简单而有效的深度跨模态哈希方法——深度共性保持哈希,可以在简单的端到端网络中同时学到模态共享表示和模态私有表示,并生成对应模态的判别性紧凑哈希码。与现有的基于深度的跨模态哈希方法相比,所提出的方法的模型复杂度和计算量几乎可以忽略不计,但是获得了显著的性能提升。在三个跨模态数据集上的大量实验结果表明,该方法优于其他当前最先进的跨模态哈希方法。

关键词: 多模态学习, 哈希, 模态共享表示, 模态私有表示, 检索

Abstract:

At present,most existing cross-modal hashing methods fail to explore the relevance and diversity of different modality data,thus leading to unsatisfactory search performance.In order to solve the above problem,a simple yet efficient deep hashing model is proposed,named deep consistency-preserving hashing for cross-modal retrieval that simultaneously exploits modality-common representation and modality-private representation through the simple end-to-end network structure,and generates compact and discriminative hash codes for multiple modalities.Compared with other deep cross-modal hashing methods,the complexity and computation of the proposed method can be neglected with significant performance improvements.Comprehensive evaluations are conducted on three cross-modal benchmark datasets which illustrate that the proposed method is superior to the state-of-the-art cross-modal hashing methods.

Key words: multi-modal learning, hash, modality-common representation, modality-private representation, retrieval

中图分类号: 

  • TP391