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# Nearest-Neighbor Search in Low Dimensions

This package implements approximate nearest-neighbor search in low dimensions for points with unsigned integer coordinates, using an elegant idea from a 2006 paper by Timothy Chan.

``````    using LowDimNearestNeighbors

# Create an array of random 3d points
arr = [[rand(UInt8), rand(UInt8), rand(UInt8)] for i in 1:100000]

# Preprocess it to prepare for efficient searching
preprocess!(arr)

# Perform an exact nearest-neighbor search
query  = [rand(UInt8), rand(UInt8), rand(UInt8)]
result = nearest(arr, query)
println("Nearest point to \$query: \$result")
println("Distance: ", norm(query - result))

# Perform an approximate nearest-neighbor search to find
# a point whose distance to the query is within 25% of
# the best possible result.
result = nearest(arr, query, 0.25)
println("Approximate nearest point to \$query: \$result")
println("Distance: ", norm(query - result))
``````

## Notes

The approach here works best in low dimensions (such as 2, 3, and 4), but the code is generic and will work for points of arbitrary dimension so long as they implement `getindex` and `length`.

This code relies on certain properties of the bit representation and will work only for points with unsigned integer coordinates.

The algorithm is in-place, i.e. it requires no extra space beyond the input array. Instead, spatial information is encoded in the permutation of points -- the preprocessing step sorts the array to prepare for efficient queries.

When performing approximate searches, the points found by the algorithm tend to be better than you'd expect based on the approximation factor. For example, the above program will often find exact matches when looking for approximate ones.

This code has been used to some success to implement nearest-neighbor search in RGB colorspace (pdf; slides from my presentation at the first-ever JuliaCon).

07/04/2014

10 months ago

91 commits