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Cross-Model Retrieval with Reconstruct Hashing

  • Yun Liu
  • Cheng Yan
  • Xiao Bai
  • Jun Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11004)

Abstract

Hashing has been widely used in large-scale vision problems thanks to its efficiency in both storage and speed. For fast cross-modal retrieval task, cross-modal hashing (CMH) has received increasing attention recently with its ability to improve quality of hash coding by exploiting the semantic correlation across different modalities. Most traditional CMH methods focus on designing a good hash function to use supervised information appropriately, but the performance are limited by hand-crafted features. Some deep learning based CMH methods focus on learning good features by using deep network, however, directly quantizing the feature may result in large loss for hashing. In this paper, we propose a novel end-to-end deep cross-modal hashing framework, integrating feature and hash-code learning into the same network. We keep the relationship of features between modalities. For hash process, we design a novel net structure and loss for hash learning as well as reconstruct the hash codes to features to improve the quality of codes. Experiments on standard databases for cross-modal retrieval show the proposed methods yields substantial boosts over latest state-of-the-art hashing methods.

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China project No. 61772057, in part by Beijing Natural Science Foundation project No. 4162037, and the support funding from State Key Lab of Software Development Environment.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  3. 3.School of Information and Communication TechnologyGriffith UniversityNathanAustralia

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