Efficient multi-instance hashing for large-scale image retrieval

Yao Yang; Yajuan Xiao; Yonghai Liu; Kuikui Wang

doi:10.1117/12.3021361

19 February 2024 Efficient multi-instance hashing for large-scale image retrieval

Yao Yang, Yajuan Xiao, Yonghai Liu, Kuikui Wang

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 1306325 (2024) https://doi.org/10.1117/12.3021361
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

Hashing has recently received excellent performance for similarity search on large-scale data set. In many scenarios, most image contains multi-instance features, and how to hash multi-instance image is an important task. Although some researchers focus on this problem and put forward solutions, they usually simply combine with existing hashing methods, which are not efficient methods for multi-instance hashing. To address this issue, we propose an Efficient Multi-Instance Hashing (EMIH) method. EMIH first construct bag features by multi-instance feature mapping. To improve the quality of hash codes, EMIH are both consider semantic correlation and local similarity preserving between bag features. Different from existing methods that relaxing the binary constraints, EMIH learn discrete codes directly by an alternate algorithm. Experimental results on two benchmark datasets demonstrate the superiority of EMIH as compared to related multi-instance hashing methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yao Yang, Yajuan Xiao, Yonghai Liu, and Kuikui Wang "Efficient multi-instance hashing for large-scale image retrieval", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 1306325 (19 February 2024); https://doi.org/10.1117/12.3021361

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available