Interest Point Descriptors and Matching CS485/685 Computer Vision Dr. George Bebis

Interest Point Descriptors and Matching

CS485/685 Computer Vision

Dr. George Bebis

Interest Point Descriptors

Extract affine regions Normalize regionsEliminate rotational

ambiguityCompute appearance

descriptors

SIFT (Lowe ’04)

Descriptors

Simplest approach: correlation

2 2

2 2

2 2 2 22 1/2 2 1/2

2 2 2 2

( , ) ( , )

( , )

[ ( , )] [ ( , )]

n n

n nk l

n n n n

n n n nk l k l

h k l f i k j l

N i j

h k l f i k j l

Simplest approach: correlation (cont’d)

• Works satisfactorily when matching corresponding regions related mostly by translation.– e.g., stereo pairs, video sequence assuming small camera

motion

Simplest approach: correlation (cont’d)

• Sensitive to small variations with respect to:– Location

– Pose

– Scale

– Intra-class variability

• Poorly distinctive!

• Need more powerful descriptors!

Scale Invariant Feature Transform (SIFT)

16 histograms x 8 orientations = 128 features

1. Take a 16 x16 window around interest point (i.e., at the scale detected).

2. Divide into a 4x4 grid of cells.

3. Compute histogram of image gradients in each cell (8 bins each).

Properties of SIFT

• Highly distinctive– A single feature can be correctly matched with high probability

against a large database of features from many images.

• Scale and rotation invariant.

• Partially invariant to 3D camera viewpoint– Can tolerate up to about 60 degree out of plane rotation

• Partially invariant to changes in illumination

• Can be computed fast and efficiently.

Example

http://people.csail.mit.edu/albert/ladypack/wiki/index.php/Known_implementations_of_SIFT

SIFT Computation – Steps

(1) Scale-space extrema detection– Extract scale and rotation invariant interest points (i.e., keypoints).

(2) Keypoint localization– Determine location and scale for each interest point.

– Eliminate “weak” keypoints

(3) Orientation assignment– Assign one or more orientations to each keypoint.

(4) Keypoint descriptor– Use local image gradients at the selected scale.

D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60(2):91-110, 2004.

Cited 9589 times (as of 3/7/2011)

1. Scale-space Extrema Detection

• Harris-Laplace

• Find local maxima of:– Harris detector in space – LoG in scale

scale

x

y

Harris

L

oG

• SIFT

Find local maxima of:– Hessian in space – DoG in scale

scale

x

y

Hessian

D

oG

1. Scale-space Extrema Detection (cont’d)

• DoG images are grouped by octaves (i.e., doubling of σ0)• Fixed number of levels per octave

σ0

2σ0

22σ0

( , , )

( , , )* ( , )

L x y

G x y I x y

( , , )

( , , ) ( , , )

D x y

L x y k L x y

down-samplewhere


• Images within each octave are separated by a constant factor kk

• If each octave is divided in ss intervals:

kkss=2=2 or k=2 or k=21/s1/s

k0σ0

ksσ0

k1σ0

k2σ0

…

(ks=2)

Choosing SIFT parameters

• Parameters (i.e., scales per octave, σ0 etc.) can be chosen experimentally based on keypoint (i) repeatability, (ii) localization, and (iii) matching accuracy.

• In Lowe’s paper:- Keypoints extracted from 32 real images (outdoor, faces,

aerial etc.)- Images were subjected to a wide range of transformations

(i.e., rotation, scaling, shear, change in brightness, noise).

Choosing SIFT parameters (cont’d)

• How many scales per octave?3 scales # of keypoints increases

but they are not stable!


• Smoothing is applied to the first level of each octave.• How should we choose σ0?

σ0 =1.6


σ0

2σ0

kσ0

k2σ0

…• Pre-smoothing discards high frequencies.

• Double the size of the input image(i.e., using linear interpolation) prior tobuilding the first level of the DoG pyramid.

• Increases the number of stable keypoints by a factor of 4.


• Extract local extrema (i.e., minima or maxima) in DoG pyramid.-Compare each point to its 8 neighbors at the same level, 9 neighbors in the level above, and 9 neighbors in the level below (i.e., 26 total).

2. Keypoint Localization

• Determine the location and scale of keypoints to sub-pixel and sub-scale accuracy by fitting a 3D quadratic polynomial:

i iX X X

( , , )i i iX x x y y

( , , )i i i iX x y

offset

keypointlocation

sub-pixel, sub-scale Estimated location

Substantial improvement to matching and stability!


• Use Taylor expansion to locally approximate D(x,y,σ) (i.e., DoG function) and estimate Δx:

• Find the extrema of D(ΔX):

2

2

( ) ( )1( ) ( )

2

TTi i

i

D X D XD X D X

2

2

( ) ( )0i iD X D X

XX X


• ΔX can be computed by solving a 3x3 linear system:

2

2

( ) ( )i iD X D XX

X X

2 2 2

2

2 2 2

2

2 2 2

2

D D D Dy x

D D D Dy

y y yx yx

DD D Dxx yx x

4

)()(

1

2

2

,11

,11

,11

,11

2

,1

,,1

2

2

,1

,1

jik

jik

jik

jik

jik

jik

jik

jik

jik

DDDD

y

D

DDDD

DDD

use finite

differences:

2 1

2

( ) ( )i iD X D XX

X X

If in any dimension, repeat.0.5X

2. Keypoint Localization (cont’d)

• Reject keypoints having low contrast.– i.e., sensitive to noise

If reject keypoint– i.e., assumes that image values have been normalized in [0,1]

| ( ) | 0.03iD X X


• Reject points lying on edges (or being close to edges)

• Harris uses the auto-correlation matrix:

2

2,

( , ) x x yW

x W y W x y y

f f fA x y

f f f

R(AW) = det(AW) – α trace2(AW)

or R(AW) = λ1 λ2- α (λ1+ λ2)2


• SIFT uses the Hessian matrix (for efficiency).– i.e., Hessian encodes principal curvatures

α: largest eigenvalue (λmax)β: smallest eigenvalue (λmin)(proportional to principal curvatures)

(SIFT uses r = 10)

(r = α/β)

Reject keypoint if:


(a) 233x189 image

(b) 832 DoG extrema

(c) 729 left after low contrast threshold

(d) 536 left after testing ratio based on Hessian

3. Orientation Assignment

• Create histogram of gradient directions, within a region around the keypoint, at selected scale:

2 2( , ) ( ( 1, ) ( 1, )) ( ( , 1) ( , 1))

( , ) tan 2(( ( , 1) ( , 1)) / ( ( 1, ) ( 1, )))

m x y L x y L x y L x y L x y

x y a L x y L x y L x y L x y

36 bins (i.e., 10o per bin)

• Histogram entries are weighted by (i) gradient magnitude and (ii) aGaussian function with σ equal to 1.5 times the scale of the keypoint.

0 2

( , , ) ( , , )* ( , )L x y G x y I x y

3. Orientation Assignment (cont’d)

• Assign canonical orientation at peak of smoothed histogram (fit parabola to better localize peak).

• In case of peaks within 80% of highest peak, multiple orientations assigned to keypoints. – About 15% of keypoints has multiple orientations assigned.

– Significantly improves stability of matching.

0 2

3. Orientation Assignment (cont’d)

• Stability of location, scale, and orientation (within 15 degrees) under noise.

4. Keypoint Descriptor

8 bins

4. Keypoint Descriptor (cont’d)

16 histograms x 8 orientations = 128 features

1. Take a 16 x16 window around detected interest point.

2. Divide into a 4x4 grid of cells.

3. Compute histogram in each cell.

(8 bins)


• Each histogram entry is weighted by (i) gradient magnitude and (ii) a Gaussian function with σ equal to 0.5 times the width of the descriptor window.


• Partial Voting: distribute histogram entries into adjacent bins (i.e., additional robustness to shifts)– Each entry is added to all bins, multiplied by a weight of 1-d,

where d is the distance from the bin it belongs.


128 features

• Descriptor depends on two main parameters:(1) number of orientations r(2) n x n array of orientation histograms

•

SIFT: r=8, n=4

rn2 features

4. Keypoint Descriptor (cont’d)• Invariance to linear illumination changes:

– Normalization to unit length is sufficient.

128 features

4. Keypoint Descriptor (cont’d)• Non-linear illumination changes:

– Saturation affects gradient magnitudes more than orientations

– Threshold entries to be no larger than 0.2 and renormalize to unit length

128 features

Robustness to viewpoint changes

• Match features after random change in image scale and orientation, with 2% image noise, and affine distortion.

• Find nearest neighbor in database of 30,000 features.

Additional robustness canbe achieved usingaffine invariantregion detectors.

Distinctiveness• Vary size of database of features, with 30 degree affine

change, 2% image noise.

• Measure % correct for single nearest neighbor match.

Matching SIFT features

• Given a feature in I1, how to find the best match in I2?

1. Define distance function that compares two descriptors.

2. Test all the features in I2, find the one with min distance.

I1I2

Matching SIFT features (cont’d)

I1 I2

f1 f2


• Accept a match if SSD(f1,f2) < t • How do we choose t?

Matching SIFT features (cont’d)• A better distance measure is the following:

– SSD(f1, f2) / SSD(f1, f2’)

• f2 is best SSD match to f1 in I2

• f2’ is 2nd best SSD match to f1 in I2

I1 I2

f1 f2f2'


• Accept a match if SSD(f1, f2) / SSD(f1, f2’) < t

• t=0.8 has given good results in object recognition.– 90% of false matches were eliminated.

– Less than 5% of correct matches were discarded


• How to evaluate the performance of a feature matcher?

5075

200


• True positives (TP) = # of detected matches that are correct

• False positives (FP) = # of detected matches that are incorrect

5075

200false match

true match

• Threshold t affects # of correct/false matches

Matching SIFT features(cont’d)

10.7

0 1FP rate

TPrate

0.1

• ROC Curve

- Generated by computing (FP, TP) for different thresholds.

- Maximize area under the curve (AUC).

http://en.wikipedia.org/wiki/Receiver_operating_characteristic

Applications of SIFT

• Object recognition• Object categorization• Location recognition• Robot localization• Image retrieval• Image panoramas

Object Recognition

Object Models

Object Categorization

Location recognition

Robot Localization

Map continuously built over time

Image retrieval – Example 1

…> 5000images

change in viewing angle

Matches

22 correct matches

Image retrieval – Example 2

…> 5000images

change in viewing angle

+ scale change

Matches

33 correct matches

Image panoramas from an unordered image set

Variations of SIFT features

• PCA-SIFT

• SURF

• GLOH

SIFT Steps - Review

(1) Scale-space extrema detection– Extract scale and rotation invariant interest points (i.e., keypoints).

(2) Keypoint localization– Determine location and scale for each interest point.

– Eliminate “weak” keypoints

(3) Orientation assignment– Assign one or more orientations to each keypoint.

(4) Keypoint descriptor– Use local image gradients at the selected scale.

D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60(2):91-110, 2004.

Cited 9589 times (as of 3/7/2011)

• Steps 1-3 are the same; Step 4 is modified.

• Take a 41 x 41 patch at the given scale, centered at the keypoint, and normalized to a canonical direction.

PCA-SIFT

Yan Ke and Rahul Sukthankar, “PCA-SIFT: A More Distinctive Representation for Local Image Descriptors”, Computer Vision and Pattern Recognition, 2004

• Instead of using weighted histograms, concatenate the horizontal and vertical gradients (39 x 39) into a long vector.

• Normalize vector to unit length.

PCA-SIFT

2 x 39 x 39 = 3042 vector

PCA-SIFT

PCA

N x 1 K x 1

'11 KxNxKxN IIA

• Reduce the dimensionality of the vector using Principal Component Analysis (PCA)– e.g., from 3042 to 36

• Some times, less discriminatory than SIFT.

SURF: Speeded Up Robust Features

• Speed-up computations by fast approximation of (i) Hessian matrix and (ii) descriptor using “integral images”.

• What is an “integral image”?

Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, “SURF: Speeded Up Robust Features”, European Computer Vision Conference (ECCV), 2006.

Integral Image

• The integral image IΣ(x,y) of an image I(x, y) represents the sum of all pixels in I(x,y) of a rectangular region formed by (0,0) and (x,y).

• . Using integral images, it takes only four array references to calculate the sum of pixels over a rectangular region of any size.

0 0

( , ) ( , )j yi x

i j

I x y I i j

SURF: Speeded Up Robust Features (cont’d)

• Approximate Lxx, Lyy, and Lxy using box filters.

• Can be computed very fast using integral images!

(box filters shown are 9 x 9 – good approximations for a Gaussian with σ=1.2)

derivative approximation approximationderivative


• In SIFT, images are repeatedly smoothed with a Gaussian and subsequently sub-sampled in order to achieve a higher level of the pyramid.


• Alternatively, we can use filters of larger size on the original image.

• Due to using integral images, filters of any size can be applied at exactly the same speed!

(see Tuytelaars’ paper for details)


• Approximation of H:

:

ˆ ˆ:

ˆ ˆ

xx xySIFTapprox

yx yy

xx xySURFapprox

yx yy

D DSIFT H

D D

L LSURF H

L L

Using DoG

Using box filters


• Instead of using a different measure for selecting the location and scale of interest points (e.g., Hessian and DOG in SIFT), SURF uses the determinant of to find both.

• Determinant elements must be weighted to obtain a good approximation:

SURFapproxH

2ˆ ˆ ˆdet( ) (0.9 )SURFapprox xx yy xyH L L L


• Once interest points have been localized both in space and scale, the next steps are:

(1) Orientation assignment

(2) Keypoint descriptor


• Orientation assignment

Circular neighborhood of radius 6σ around the interest point(σ = the scale at which the point was detected)

Haar wavelets (responses weighted with Gaussian)Side length = 4σ

x response y response

Can be computed very fast using integral images!

600

angle

( , )dx dy


• Sum the response over each sub-region for dx and dy separately.

• To bring in information about the polarity of the intensity changes, extract the sum of absolute value of the responses too.

Feature vector size:

4 x 16 = 64

• Keypoint descriptor (square region of size 20σ)

4 x 4grid


• SURF-128– The sum of dx and

|dx| are computed separately for points where dy < 0 and dy >0

– Similarly for the sum of dy and |dy|

– More discriminatory!

SURF: Speeded Up Robust Features

• Has been reported to be 3 times faster than SIFT.

• Less robust to illumination and viewpoint changes compared to SIFT.

K. Mikolajczyk and C. Schmid,"A Performance Evaluation of Local Descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, 2005.

Gradient location-orientation histogram (GLOH)

• Compute SIFT using a log-polar location grid:– 3 bins in radial direction (i.e., radius 6, 11, and 15)

– 8 bins in angular direction

• Gradient orientation quantized in 16 bins.

• Total: (2x8+1)*16=272 bins PCA.

K. Mikolajczyk and C. Schmid,"A Performance Evaluation of Local Descriptors", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, 2005.

Shape Context

• A 3D histogram of edge point locations and orientations. – Edges are extracted by the Canny edge detector.

– Location is quantized into 9 bins (using a log-polar coordinate system).

– Orientation is quantized in 4 bins (i.e., (horizontal, vertical, and two diagonals).

• Total number of features: 4 x 9 = 36


Spin image

• A histogram of quantized pixel locations and intensity values.– A normalized histogram is computed for each of five rings

centered on the region.

– The intensity of a normalized patch is quantized into 10 bins.

• Total number of features: 5 x 10 = 50


Differential Invariants

Example: some Gaussian derivatives up to fourth order

• “Local jets” of derivatives obtained by convolving the image with Gaussian derivates.• Derivates are computed at different orientations by rotating the image patches. ( , ) ( )

( , ) ( )

( , )* ( )

( , )* ( )

( , )* ( )

( , )* ( )

x

y

xx

xy

yy

I x y G

I x y G

I x y G

I x y G

I x y G

I x y G

computeinvariants

Bank of Filters

(e.g., Gabor filters)

Moment Invariants

• Moments are computed for derivatives of an image patch using:

where p and q is the order, a is the degree, and Id is the image gradient in direction d.

• Derivatives are computed in x and y directions.

Bank of Filters: Steerable Filters

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Interest Point Descriptors and Matching CS485/685 Computer Vision Dr. George Bebis