An Empirical Study on Large-Scale Content-Based Image Retrieval

• Main Authors: WONG, Yuk Man, HOI, Steven C. H., LYU, Michael R.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2386; https://ink.library.smu.edu.sg/context/sis_research/article/3386/viewcontent/An_empirical_study_on_large_scale_content_based_image_retrieval_av.pdf
• Institution: Singapore Management University

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.