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

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 test bed 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.

http://www.cais.ntu.edu.sg/~chhoi/paper_pdf/ICME07LCBIR.pdf