Comparison of image segmentation

Presented by
Haitham Abdel-atty Abdullah
Yara Bahaa El-Din Hashem
Pre-Masters 2014-2015
Supervised by:
Prof. Dr. Mostafa Gadal-Haqq
1

 Introduction
 Image Segmentation Algorithms
› Mean Shift Segmentation
› Efficient Graph-based Segmentation
› Hybrid Segmentation Algorithm
 Normalized Probabilistic Rand (NPR)
Index
 Experiments
 Conclusion
2

 Image segmentation
Is the process of partitioning a digital image into
multiple segments (sets of pixels)
 The goal of segmentation
Is to simplify and/or change the representation
of an image into something that is more
meaningful and easier to analyze
3

 We will present an evaluation of two popular
segmentation algorithms, the mean shift-based
segmentation algorithm and a graph-based
segmentation scheme. We also consider a hybrid
method which combines the other two methods.
 we compare all use the same image features
(position and color) for segmentation, thereby
making their outputs directly comparable.
4

 For each of these algorithms, we examine three characteristics:
1. Correctness: the ability to produce results that are consistent
with ground truth
2. Stability with respect to parameter choice: the ability to
produce segmentations of consistent correctness for a range of
parameter choices.
3. Stability with respect to image choice: the ability to produce
segmentations of consistent correctness using the same
parameter choice on a wide range of different images.
The Normalized Probabilistic Rand (NPR) index is used to measure the
above characteristics.
5

 Is a nonparametric clustering technique which does
not require prior knowledge of the number of clusters,
and does not constrain the shape of the clusters.
 Mean shift is used for image segmentation, clustering,
visual tracking, space analysis, mode seeking ...
 Technique for clustering-based segmentation
7

 The key to mean shift is a technique for efficiently
finding peaks (highest density or mode) in this high-
dimensional data distribution
8

Density Estimation
Gradient Estimation
(Mean Shift)
Data
Discrete PDF Representation
(PDF : probability density function)
PDF Analysis
9

Assumed Underlying PDF Real Data Samples
1
1
( ) ( )
n
i
i
P K
n 
 x x - x Kernel Density Estimation is a function of some finite
number of data points x1…xn
Data
10

Region of
interest
Center of
mass
Mean Shift
vector
11

Region of
interest
Center of
mass
Mean Shift
vector
12

Region of
interest
Center of
mass
Mean Shift
vector
13

Region of
interest
Center of
mass
Mean Shift
vector
14

Region of
interest
Center of
mass
Mean Shift
vector
15

Region of
interest
Center of
mass
Mean Shift
vector
16

Region of
interest
Center of
mass
17

Simple Mean Shift procedure:
• Compute mean shift vector
•Translate the Kernel window by
m(x)
2
1
2
1
( )
n
i
i
i
n
i
i
g
h
g
h


  
  
     
  
   
  


x - x
x
m x x
x - x
19

 Attraction basin: the region for which all
trajectories lead to the same peak (mode)
 Cluster: all data points in the attraction basin
of a mode
21

 Pros
 Does not assume spherical clusters
 Just a single parameter (window size)
 Robust to outliers
Cons
 Computationally expensive.
 Have to choose kernel size in advance
 Output depends on window size.
 Not suitable for high-dimensional features.
24

 Another method of performing clustering
in feature space.
 Works on data points in feature space
without first performing a filtering step.
 Key to success of this method is adaptive
thresholding.
26

 Represent features and their relationships
using a graph
 Manipulate the graph to segment the
image
27

 Node for every pixel
 Edge between every pair of pixels (or
every pair of “sufficiently close” pixels)
 Each edge is weighted by the similarity
of the two nodes
wij
i
j
28

 Break Graph into Segments
› Delete links that cross between segments
› Easiest to break links that have low affinity
 similar pixels should be in the same segments
 dissimilar pixels should be in different segments
A B C
wij
i
j
29

Intensity
Color
Distance
aff x, y  exp  1
2i
2




I x  I y 2
 





2d
2




x  y
2
 





2t
2




c x  c y 2
 





30

 Small σ: group only nearby points
 Large σ: group far-away points
31

Changing scores for different parameters using efficient
graph-based segmentation: (a) Original image, (b)-(d) efficient
graph-based segmentations using scale bandwidth (hs) 7, color
bandwidth (hr) 7 and k values 5, 25, and 125 respectively.
33

 Combine two previous methods
 we apply mean shift filtering, and then
we use efficient graph-based clustering
to give the final segmentation.
 The quality of the segmentation is high.
35

Example of changing scores for different parameters using a hybrid
segmentation algorithm which first performs mean shift filtering and then
efficient graph-based segmentation: (a) Original image, (b)-(g)
segmentations using scale bandwidth (hs) 7, and color bandwidth (hr)
and k value combinations (3,5), (3,25), (3,125), (15,5), (15,25), (15,125)
respectively.
36

 The Rand index (RI) or Rand measure
(named after William M. Rand) is a measure of
the similarity between two data clustering.
G P
a
b
c
d
a
b
c
d
a a
X
dcba
da
GPRI


),(
The Rand index has a value between 0 and 1.
38

 The Rand index (RI) a
ba + b + c + d
RI(P,G)
dcba
da
GPRI


),(
39

 The Probabilistic Rand Index (PRI)
counts the fraction of pairs of pixels whose labels are consistent
between the computed segmentation and the ground truth,
averaging across multiple ground truth (manual) segmentations to
account for scale variation in human perception.
In other simple words, PRI measuring the similarity between two
partitions.
40

 In PRI agreements ( ) and disagreements ( ) at
the pixel-pair are weighted according to the probability of their
occurring.
Computed segmentation
Multiple ground truth (manual)
segmentations
41

 The PR index does however have one serious flaw. Note that the PR
index is on a scale of 0-1, but there is no expected value for a given
segmentation. That is, it is impossible to know if any given score is good
or bad.
 The significance of a measure of similarity has much to do with the
baseline with respect to which it is expressed.
 For image segmentation, the baseline may be interpreted as the
expected value of the index.
 All of these issues are resolved with normalization to produce the
Normalized Probabilistic Rand (NPR) index
Baseline
NPR Index
Is one
PRI
42

 ‘EDISON’ refers EDISON system for mean
shift segmentation.
 ‘FH’ refers to the efficient graph-based
segmentation method.
 ‘MS+FH’ refers to our hybrid algorithm of
mean shift filtering followed by efficient
graph-based segmentation.
 All of the experiments were performed
on the publicly available Berkeley image
segmentation database which contains
300 images of natural scenes.
44

Examples of images from the Berkeley image
segmentation database 45

 we will divide each dimension by the
corresponding {hs, hr} as in the EDISON
system. So each algorithm was run with a
parameter combination from the sets:
hs = 7,
hr = {3, 7, 11, 15, 19, 23}, and
k = {5, 25, 50, 75, 100, 125}.
46

Maximum NPR indices achieved on individual images with the set of
parameters used for each algorithm. Plot (a) shows the indices
achieved on each image individually, ordered by increasing index.
Plot (b) shows the same information in the form of a histogram. 47

 All of the algorithms produce similar
maximum NPR indices, demonstrating
that they have roughly equal ability to
produce correct segmentations with the
parameter set chosen.
 Few images which have below-zero
maximum NPR index.
48

 An algorithm which creates good
segmentations will have a histogram
skewed to the right.
 A standard deviation histogram that is
skewed to the left indicates that the
algorithm in question is less sensitive to
changes in its parameters.
 Using the means as a measure certainly
makes us more dependent on our choice
of parameters for each algorithm.
49

Average performance over all parameter
combinations:
› Mean NPR plots for each of the three
systems with averages taken over all possible
combinations of the parameters hr and k
50

Mean NPR indices achieved using each of the segmentation algorithms.
The first row shows results from the mean shift-based system (EDISON), the
second from the efficient graph-based system (FH), and the third from the
hybrid segmentation system (MS+FH). Results from each algorithm are
given for individual images over the parameter set of all combinations of hr
= {3, 7, 11, 15, 19, 23} and k = {5, 25, 50, 75, 100, 125}. Plots (a), (d) and (g)
show the mean indices achieved on each image individually, ordered by
increasing index, along with one standard deviation. Plots (b), (e) and (h)
show histograms of the means. Plots (c), (f) and (i) show histograms of the
standard deviations.
52

Average performance over different
values of the color bandwidth hr:
› NPR indices averaged over values of hr, with
k held constant
53

Mean NPR indices achieved using the efficient graph-based
segmentation system (FH) on individual images over the parameter
set hr = {3, 7, 11, 15, 19, 23} with a constant k. Plot (a) shows the mean
indices achieved on each image individually, ordered by increasing
index, along with one standard deviation. Plot (b) shows a histogram
of the means. Plot (c) shows a histogram of the standard deviations.
55

Mean NPR indices achieved using the hybrid segmentation system
(MS+FH) on individual images over the parameter set hr = {3, 7, 11, 15,
19, 23} with a constant k. Plot (a) shows the mean indices achieved on
each image individually, ordered by increasing index, along with one
standard deviation. Plot (b) shows a histogram
of the means. Plot (c) shows a histogram of the standard deviations.
57

Average performance over different
values of k
› Mean NPR indices as k is varied and hr is
held constant.
58

Mean NPR indices achieved using the efficient graph-based
segmentation system (FH) on individual images over the parameter set
k = {5, 25, 50, 75, 100, 125} with a constant hr. Plots (a), (d) and (g) show
the mean indices achieved on each image
individually, ordered by increasing index, along with one standard
deviation. Plots (b), (e) and (h) show histograms of the means. Plots (c),
(f) and (i) show histograms of the standard deviations.
60

Mean NPR indices achieved using the hybrid segmentation system
(MS+FH) on individual images over the parameter set k = {5, 25, 50,
75, 100, 125} with a constant hr. Plots (a), (d) and (g) show the mean
indices achieved on each image individually, ordered by increasing
index, along with one standard deviation. Plots
(b), (e) and (h) show histograms of the means. Plots (c), (f) and (i)
show histograms of the standard deviations.
62

 The final set of experiments looks at the
stability of a particular parameter
combination across images.
 In each experiment results are shown
with respect to a particular parameter,
with averages and standard deviations
taken over segmentations of each
image in the entire image database.
63

Average performance over all images for
different values of hr:
Mean NPR indices using the
EDISON segmentation system on
each color bandwidth (hr) over
the set of images, with one
standard deviation.
64

Mean NPR indices using graph-based segmentation (FH) on
each color bandwidth hr= {3, 7, 11, 15, 19, 23} over the set of
images. One plot per value of k.
65

Mean NPR indices using hybrid segmentation (MS+FH)
on each color bandwidth hr= {3, 7, 11, 15, 19, 23} over
the set of images. One plot per value of k.
66

Average performance over all images for
different values of k
› Examine the stability of k over a set of
images.
67

Mean NPR indices using efficient graph-based
segmentation (FH) on each of k = {5, 25, 50, 75, 100,
125} over the set of images. One plot per value of
hr. 68

Mean NPR indices using hybrid segmentation
(MS+FH) on each of k = {5, 25, 50, 75, 100, 125} over
the set of images. One plot per value of hr.
69

 The first comparison considered the
correctness of the three algorithms.
 Hybrid algorithm performed slightly
better than the mean shift algorithm,
and both performed significantly better
than the graph-based segmentation.
 We can conclude that the mean shift
filtering step is indeed useful, and that
the most promising algorithms are the
mean shift segmentation and the hybrid
algorithm.
70

 The second comparison considered stability
with respect to parameters.
 The hybrid algorithm showed less variability
when its parameters were changed than
the mean shift segmentation algorithm.
 Although the amount of improvement did
decline with increasing values of k, the rate
of decline was very slow.
 Although the graph-based segmentation
did show very low variability with k = 5,
changing the value of k decreased its
stability drastically.
71

 Finally, we compared the stability of a
particular parameter choice over the set of
images.
 Once again we see that the graph-based
algorithm has low variability when k = 5,
however its performance and stability
decrease rapidly with changing values of k.
 The comparison between the mean shift
segmentation and the hybrid method is
much closer here, with neither performing
significantly better.
72

 For the three characteristics measured,
we have demonstrated that both the
mean shift segmentation and hybrid
segmentation algorithms can create
realistic segmentations with a wide
variety of parameters.
 However the hybrid algorithm has slightly
improved stability.
 Thus, we would choose to incorporate
the hybrid method into a larger system.
73

Comparison of image segmentation

More Related Content

What's hot (20)

Similar to Comparison of image segmentation (20)

More from Haitham Ahmed (6)

Recently uploaded (20)

Comparison of image segmentation