Rank-based Hashing for Effective and Efficient Nearest Neighbor Search for Image Retrieval

Author:

Kawai Vinicius Sato1ORCID,Valem Lucas Pascotti2ORCID,Baldassin Alexandro2ORCID,Borin Edson3ORCID,Pedronette Daniel Carlos Guimarães4ORCID,Latecki Longin Jan5ORCID

Affiliation:

1. Department of Statistics, Applied Math. and Computing, State University of São Paulo (UNESP), Rio Claro, Brazil

2. Department of Statistics, Applied Math. and Computing, State University of São Paulo (UNESP), Rio Claro Brazil

3. DSC, University of Campinas (UNICAMP), Campinas, Brazil

4. DEMAC, State University of São Paulo (UNESP), Rio Claro, Brazil

5. Temple University, Philadelphia, United States

Abstract

The large and growing amount of digital data creates a pressing need for approaches capable of indexing and retrieving multimedia content. A traditional and fundamental challenge consists of effectively and efficiently performing nearest-neighbor searches. After decades of research, several different methods are available, including trees, hashing, and graph-based approaches. Most of the current methods exploit learning to hash approaches based on deep learning. In spite of effective results and compact codes obtained, such methods often require a significant amount of labeled data for training. Unsupervised approaches also rely on expensive training procedures usually based on a huge amount of data. In this work, we propose an unsupervised data-independent approach for nearest neighbor searches, which can be used with different features, including deep features trained by transfer learning. The method uses a rank-based formulation and exploits a hashing approach for efficient ranked list computation at query time. A comprehensive experimental evaluation was conducted on seven public datasets, considering deep features based on CNNs and Transformers. Both effectiveness and efficiency aspects were evaluated. The proposed approach achieves remarkable results in comparison to traditional and state-of-the-art methods. Hence, it is an attractive and innovative solution, especially when costly training procedures need to be avoided.

Funder

Fulbright Commission, in part by São Paulo Research Foundation - FAPESP

Brazilian National Council for Scientific and Technological Development - CNPq

Petrobras

National Science Foundation, USA

Publisher

Association for Computing Machinery (ACM)

Reference57 articles.

1. Alexandr Andoni and Daniel Beaglehole. 2022. Learning to hash robustly, guaranteed. In International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research), Vol. 162. 599–618.

2. A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search

3. Searching in metric spaces

4. Deep image retrieval: A survey;Chen Wei;CoRR,2021

5. Deep Supervised Hashing With Anchor Graph

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3