Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2007
Abstract
One key challenge in content-based image retrieval (CBIR) is to develop a fast solution for indexing high-dimensional image contents, which is crucial to building large-scale CBIR systems. In this paper, we propose a scalable content-based image retrieval scheme using locality-sensitive hashing (LSH), and conduct extensive evaluations on a large image testbed of a half million images. To the best of our knowledge, there is less comprehensive study on large-scale CBIR evaluation with a half million images. Our empirical results show that our proposed solution is able to scale for hundreds of thousands of images, which is promising for building Web-scale CBIR systems.
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
2007 IEEE International Conference on Multimedia and Expo ICME: 2-5 July, Beijing: Proceedings
First Page
2206
Last Page
2209
ISBN
9781424410163
Identifier
10.1109/ICME.2007.4285123
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
WONG, Yuk Man; HOI, Steven C. H.; and LYU, Michael R..
An Empirical Study on Large-Scale Content-Based Image Retrieval. (2007). 2007 IEEE International Conference on Multimedia and Expo ICME: 2-5 July, Beijing: Proceedings. 2206-2209.
Available at: https://ink.library.smu.edu.sg/sis_research/2386
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICME.2007.4285123