Date post: | 17-Jan-2016 |
Category: |
Documents |
Upload: | jodie-stone |
View: | 218 times |
Download: | 0 times |
PROBING THE LOCAL-FEATURE SPACE OF INTEREST POINTS
Wei-Ting Lee, Hwann-Tzong Chen
Department of Computer ScienceNational Tsing Hua University, Taiwan
ICIP 2010
OUTLINE• Introduction• Approach– Locality-Sensitive
Hashing (LSH) – Sketching the
Feature Space• Experiments– Fast Matching
• Conclusion
INTRODUCTION
Local feature have been extensively used to represent image for various problem
Lots of local feature detector and local feature descriptor have been proposed recent years
Recent History
Maximally Stable Extremal Regions (MSER) [1] BMVC 2002
Difference-of-Gaussian and Scale-Invariant-Feature-Transform (SIFT) [2]
IJCV 2004
Affine invariant detector [3] , [4] IJCV 2004 , TPAMI 2005
Histogram of oriented gradients (HOG) [5]CVPR 2005
‘Visual words’ [6] ‘codebooks’ [7] ICCV 2003 , BMVC 2003
For example
• Present an empirical analysis of the feature space of interest points detected in natural image
• Perform an approximate method for the fast matching between two sets of interest points detected in two images
• Show that the complexity of matching M points to N points can be reduced from O(MN) to O(M+N)
INTRODUCTION
Locality-Sensitive Hashing
• p-stable Distribution:
Locality-Sensitive Hashing based on 2-Stable Distribution
Hash Family
a : random vector sampled from a Gaussian distribution
b : real value chosen uniformly from the range [0 , r]
r : line width
The dot-product a‧v projects each vector to the real line
Building Hash table
Building Hash table
Choose the width r based on the minimum and maximum
=?
θ
a‧b = |a| |b|
Index function
t = 5 , K=3
[5] [5] [5] = 125 = (5-1) * 52 + (5-1) * 51 + 4 * 50 + 1 = 4 * 25 + 20 + 4 + 1 = 125
Sketching the Feature Space
Berkeley segmentation database [14]
Use difference of Gaussian (DOG) [2] & Hessian-affine [3] detector detect about 200,000 interest points
Extract image patches by SIFT descriptor [2]
Create a hash table (L = 1) with five projection(K = 5) and 15 segments on each dot-product real line (t = 15)
The total number of buckets is 155 = 759,375
Entropy = 4.2251(a) DOG
Entropy = 4.0622(b) Hessian-affine
Sketching the Feature SpaceDistribution and Entropy
Collect three image patches of different size 16x16 , 32x32 , 64x64
Each set consist of 200,000 patches.
Natural image patches (from Berkeley segmentation database )
Noise image patches (Randomly-generated noise patches)
Sketching the Feature Space
Distribution and Entropy
Fast Matching
3
3
3 3
3 3
3
3
Referenceimage
RemainingImage (test)
Fast MatchingWe create L = 16 hash tables to probe the 128-dimensional SIFT-feature space
Each table is equipped with five 2-stable Projections , and the projected values are quantized into 15 segments,
i.e., K = 5 and t = 15
For LSH, we use two threshold values of dot-product, θ = 0.95 and θ = 0.97,to determine whether a pair of feature vectors in the same bucket yields a match
LSH is 2 to 15 times faster than matching by exhaustive search
a b = |a| |b| ‧If a = b , then = 1
Fast Matching
DoG detector + SIFT descriptor Hessian-affine detector + SIFT descriptor
DoG detector + SIFT descriptor
2-stable LSH matching vs. exhaustive matching
2-stable LSH matching vs. exhaustive matching
Hessian-affine detector + SIFT descriptor
Conclusion
Using the approximate nearest-neighbor probing scheme derived from 2-stable Locality-Sensitive Hashing
Make use of the efficient representation of the SIFT feature space, and present a fast feature-matching method for finding correspondences between two sets of interest points.
And,Have been used by Whiteorange !!
THANK YOU SO MUCH