  In pattern recognition or machine learning, it is a very fundamental task to
find nearest neighbors of a given point. All the methods for the task work
basically by comparing the given point to all the points in the data set. That
is why the computational cost increases with the number of data points.
However, the human visual system seems to work in a different way. When the
human visual system tries to find the neighbors of one point on a map, it
directly focuses on the area around the point and actively searches the
neighbors by looking or zooming in and out around the point. In this paper, we
propose an innovative search method for nearest neighbors, which seems very
similar to how human visual system works on the task.
