  Detecting near duplicate images is fundamental to the content ecosystem of
photo sharing web applications. However, such a task is challenging when
involving a web-scale image corpus containing billions of images. In this
paper, we present an efficient system for detecting near duplicate images
across 8 billion images. Our system consists of three stages: candidate
generation, candidate selection, and clustering. We also demonstrate that this
system can be used to greatly improve the quality of recommendations and search
results across a number of real-world applications.
  In addition, we include the evolution of the system over the course of six
years, bringing out experiences and lessons on how new systems are designed to
accommodate organic content growth as well as the latest technology. Finally,
we are releasing a human-labeled dataset of ~53,000 pairs of images introduced
in this paper.
