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What Is the Most Efficient Way to Select Nearest Neighbor Candidates for Fast
Approximate Nearest Neighbor Search?
Masakazu Iwamura, Tomokazu Sato and Koichi Kise(Osaka Prefecture University, Japan)
ICCV’2013
Sydney, Australia
Finding similar data Basic but important problem in information
processing
Possible applications include Near-duplicate detection Object recognition Document image retrieval Character recognition Face recognition Gait recognition
A typical solution: Nearest Neighbor (NN) Search
2
Finding similar data by NN Search Desired properties
Fast and accurate Applicable to large-scale data
3
The paper presents a way to realizefaster approximate nearest neighbor
search for certain accuracy
Benefit from improvement of
computing power
Contents NN and Approximate NN Search Performance comparison Keys to improve performance
4
Contents NN and Approximate NN Search Performance comparison Keys to improve performance
5
Nearest Neighbor (NN) Search This is a problem that the true NN is
always found In a naïve way
6
NN
Data Query
For more data,more time is required
7
Nearest Neighbor (NN) Search Finding nearest neighbor efficiently
Before query is given
1. Index dataNN
1. Select search regions2. Calculate distances of
selected data
After query is given
The true NN must be contained in the selected search regions
Ensuring this takes so long time
Search regions
8
Approximate Nearest Neighbor Search Finding nearest neighbor more efficiently
NN
Search regions Much faster
“Approximate” means that the true NN is not
guaranteed to be retrieved
Contents NN and Approximate NN Search Performance comparison Keys to improve performance
10
ANN search on 100M SIFT features
BAD
GOOD
Selected results
ANN search on 100M SIFT features
BAD
GOOD
IMI(Babenko 2012)
IVFADC(Jegou 2011)
Selected results
ANN search on 100M SIFT features
BAD
GOOD
IMI(Babenko 2012)
IVFADC(Jegou 2011)
BDH(Proposed method)
2.0 times
4.5 times
9.4 times
2.9 times
Selected results
ANN search on 100M SIFT features
BAD
GOOD
IMI(Babenko 2012)
IVFADC(Jegou 2011)
BDH(Proposed method)
2.0 times
4.5 times
9.4 times
2.9 times
The novelty of BDH was reduced by IMI before we
succeeded in publishing it…(For more detail, check out the
Wakate program on Aug. 1) Selected results
ANN search on 100M SIFT features
BAD
GOOD
IMI(Babenko 2012)
IVFADC(Jegou 2011)
BDH(Proposed method)
2.0 times
4.5 times
9.4 times
2.9 times
So-called binary coding is not suitable for fast
retrieval but for saving memory usage Selected
results
Contents NN and Approximate NN Search Performance comparison Keys to improve performance
16
Keys to improve performance Select search regions in subspaces Find the closest ones in the original space
efficiently
17
Keys to improve performance Select search regions in subspaces Find the closest ones in the original space
efficiently
18
Select search regions in subspaces In past methods (IVFADC, Jegou 2011 &
VQ-index, Tuncel 2002)
Search regions
Query
Indexed by k-means
clustering
Select search regions in subspaces In past methods (IVFADC, Jegou 2011 &
VQ-index, Tuncel 2002)
Search regions
Query
Indexed by k-means
clustering
Taking very much time to select the search regions
Proven to be the least quantization error
Pros.
Cons.
Indexed by vector quantization
Select search regions in subspaces In the past state-of-the-art (IMI, Babenko
2012)
Feature vectors
Divide into two or more
Calculate distances
in subspaces
Select the regions in the original
space
Indexed by k-means
clustering
Indexed by k-means
clustering
Select search regions in subspaces In the past state-of-the-art (IMI, Babenko
2012)
Feature vectors
Divide into two or more
Calculate distances
in subspaces
Select the regions in the original
space
Less accurate(More quantization error)
Much less processing timePros.
Cons.
>
Indexed by product quantization
Realize better ratio
Keys to improve performance Select search regions in subspaces Find the closest ones in the original space
efficiently
23
Find the closest search regionsin original space In the past state-of-the-art (IMI, Babenko
2012)
1 3 815
1 2 4 916
2 3 510
5 6 8
11
12
Centroid in original space
1 38
15
12
5
11
Search regions are selected in the ascending order of distances in the original space
Subspace 2
Sub
space
1
Distances in subspace
2
Dis
tan
ces
in s
ub
space
1
Centroid in
subspace
Find the closest search regionsin original space In the past state-of-the-art (IMI, Babenko
2012)
1 3 815
1 2 4 916
2 3 510
5 6 8
11
12
Centroid in original space
1 38
15
12
5
11
Subspace 2
Sub
space
1
Distances in subspace
2
Dis
tan
ces
in s
ub
space
1
Centroid in
subspace
This can be done more efficiently with the branch and bound
methodIt does not consider the
order of selecting buckets
Search regions are selected in the ascending order of distances in the original space
Find the closest search regionsin original space efficiently In the proposed method
Centroid in original space
1 38
15
12
5
11
Subspace 2
Sub
space
1
Centroid in
subspace
0
1
3
8
15
1
2
5
11
Assume that upper limit is set to 8
Distances in subspace
1
Distances in subspace
2
Find the closest search regionsin original space efficiently In the proposed method
Centroid in original space
1 38
15
12
5
11
Subspace 2
Sub
space
1
Centroid in
subspace
Distances in subspace
2
Distances in subspace
1
1
3
8
15
1
2
5
11
Assume that upper limit is set to 8
Max 8
0
Find the closest search regionsin original space efficiently In the proposed method
Centroid in original space
1 38
15
12
5
11
Subspace 2
Sub
space
1
Centroid in
subspace
Distances in subspace
2
Distances in subspace
1
1
3
8
15
1
2
5
11
Assume that upper limit is set to 8
Max 8Max 8
10
Find the closest search regionsin original space efficiently In the proposed method
Centroid in original space
1 38
15
12
5
11
Subspace 2
Sub
space
1
Centroid in
subspace
Distances in subspace
2
Distances in subspace
1
1
3
8
15
1
2
5
11
Assume that upper limit is set to 8
Max 8Max 8
0 2
Find the closest search regionsin original space efficiently In the proposed method
Centroid in original space
1 38
15
12
5
11
Subspace 2
Sub
space
1
Centroid in
subspace
Distances in subspace
2
Distances in subspace
1
1
3
8
15
1
2
5
11
Assume that upper limit is set to 8
Max 8Max 8
0 5
Find the closest search regionsin original space efficiently In the proposed method
The upper and lower bounds are increased in a step-by-step manner until enough number of data are selected
31
What Is the Most Efficient Way to Select Nearest Neighbor Candidates for Fast
Approximate Nearest Neighbor Search?
Masakazu Iwamura, Tomokazu Sato and Koichi Kise(Osaka Prefecture University, Japan)
ICCV’2013
Sydney, Australia