Nearest NeighborNearest Neighbor

Paul Hsiung

March 16, 2004

Quick Review of NNQuick Review of NN

Set of points P Query point q Distance metric d Find p in P such that

d(p,q) < d(p’,q)for all p’ in P

qp

NN Used In…NN Used In…

Image databases [Pentland et al]Color indexing [swain et al]Recognizing 3D objects [Murase et al]Shapes [Mori et al]Drug testingDNA sequence matching [Buhler]

Tree-based ApproachesTree-based Approaches

Quadtrees– Split middle in all dimensions– Split until no points or one point left

Kd-trees– Split in one dimension– Pick the middle wisely

Ball-trees– Pick two pivots and split

SR-trees– We have rectangles and spheres, so why not combine them

Indyk’s GripeIndyk’s Gripe

Beyond 10 or 20 dimensions, tree-based structures will look at many points

No better than brute force linear search…So he came up with a hash table approach:

Locality Sensitive Hashing (LSH)Rest of talk will be on his paper

LSHLSH

Interlude: Near NeighborInterlude: Near Neighbor

Set of points P Query point q Distance metric d Find p in P such that

d(p,q) < (1+ε)d(P,q)where d(P,q) is the distance of q to its closest point in P

q p(1+ε)d(P,q)

d(P,q)

HashHash

Pick a subset I of random coordinatesHash function, h(p), will return a bucket ID

h(p) = projection of p on I

IntuitionIntuition

If two points are close, they hash to same bucket with some probability p1

If they are far, they hash to same bucket with a smaller probability p2 < p1

Indyk’s HashIndyk’s Hash

Convert coordinates of p to {0,1}d

Use Hamming distance: d(p,q)= # positions on which p and q differ

Example:– p=(0,1,0,1,1,1,0,0,1,0)– I={2,5,7}– Then, h(p)=(1,1,0)

Demo: – http://web.mit.edu/ardonite/6.838/locality-hashing.htm

Why Locality-sensitive?Why Locality-sensitive?

Pr[h(p)=h(q)]=(1-d(p,q)/D)k

– D is the number of dimensions in the binary representation

– k is the size of I We can vary the probability by changing k

k=1 k=2

distance distance

Pr Pr

Now to Use It (Training)Now to Use It (Training)

Generate l hash functions: h1..hl

Store each point p in the bucket hi(p) of the i-th hash array, i=1...l

Now to Use It (Query)Now to Use It (Query)

Retrieve all the points that belong to the buckets: h1(q)..hl(q)

Return the retrieved point that is closest to qThis “solves” the Near Neighbor problem

Indyk’s ResultsIndyk’s Results

Compared with another tree-based algorithmColor histogram dataset from Corel Draw

– 20,000 images, 64 dimensions– Used 1k, 2k, 5k, 10k, 19k points for training– 1k points are used for query– Computed missed ratio – fraction of queries with

no hits

Indyk’s ResultsIndyk’s Results

Results IIResults II

Ugly SideUgly Side

Works best with Hamming distance– Can be extended from L1 and L2 norms

Requires parameter tweaking (size of I and number of hash buckets)

Does not work well on uniform data

BibliographyBibliography

A. Gionis, P. Indyk, R. Motwani. Similarity Search in High Dimensions via Hashing. In VLDB 25th, 1999

J. Buhler. Efficient Large-Scale Sequence Comparison by Locality-Sensitive Hashing. In Bioinformatics 17(5) 419-428, 2001

H. Murase, S. K. Nayar. Visual Learning and Recognition of 3D Objects from Appearance. In IJCV, Vol. 14, No. 1 5-24, 1995

A. Pentland, R.W. Picard, S. Scalroff. Photobook: Tools for Content Based Manipulation of Image Databases. In SPIE Vol. 2185 34-47, 1994

M.J. Swain, D.H. Ballard. Color Indexing. In IJCV, Vol. 7, No. 1 11-32, 1991

G. Mori, S. Belongie, J. Malik. Shape Contexts Enable Efficient Retrieval of Similar Shapes. CVPR 1 723-730, 2001

Slides: “Algorithms for Nearest Neighbor Search” by Piotr Indyk

Slides: “Approximate Nearest Neighbor in High Dimensions via Hashing” by Aris Gionis, Piotr Indyk, and Rajeev Motwani