1. The document describes a fast unsupervised approach for image segmentation based on clustering algorithm fusion and level set methods. 2. It first extracts features from images using color transformation to CIE L*a*b* color space and non-linear diffusion. Then it applies multiple clustering algorithms including Fuzzy C-means, K-means, self-organizing maps, and Gaussian mixture models. 3. The results from the clustering algorithms are fused to generate a cluster map which is then used to evolve the level set contour for segmentation. Simulation results on texture images show the proposed method achieves better segmentation accuracy compared to traditional level set approaches.

1. 1
Locating Texture Boundaries Using a Fast
Unsupervised Approach Based on
Clustering Algorithms Fusion and Level Set
Slides by:
Mehryar Emambakhsh
Sahand University of Technology

2. 2
Outline
 About image segmentation and its methods
 Feature extraction
– Color transformation
– Non-linear diffusion
 Clustering algorithms
– Fusion
 Level set
 Simulation results
 Summary
 References

3. 3
About image segmentation and its
methods
 Image segmentation is a procedure in which
an image is partitioned into its constituting
regions.
 There must be a uniformity in some
predefined features in each region:
– Pixels intensity
– Color components
– Texture features
– Motion vectors

4. 4
About image segmentation and its
methods
 There are many different approaches for
image segmentation:
1) Clustering-based methods  feature space 
clustering algorithms
 Advantage:
– Fast computational speed
 Disadvantages:
– Sensitivity to noise and outliers in the feature space
– Over-segmentation

5. 5
About image segmentation and its
methods
2) Energy minimization methods  feature space
 energy function minimization
 Advantages:
– Reasonable results
– Robust against noise
 Disadvantages:
– High computational complexity
– Sensitive to local minima

6. 6
About image segmentation and its
methods
 Our proposed method:

7. 7
Feature extraction: color
transformation
 Color transformation:
– Non-linear color spaces generate a more
separable feature space compared to linear
color spaces.
– Among non-linear color spaces, CIE L*a*b*,
which is a uniform color space, produces a much
detachable feature space compared to the non-
uniform ones.

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Feature extraction: non-linear diffusion
 Non-linear diffusion is a method for image de-noising and
simplification.
 It is used for feature extraction from texture in our approach.
 Non-linear diffusion equation is solved on the color image:
 g(.) is a decreasing function of image gradient.
 Non-linear diffusion has many superiority compared to other
texture feature extraction methods:
– Low dimensionality
– Preserving image edges
– Robust against noise

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Clustering algorithms
 Fuzzy C-means (FCM), K-means, SOM (Self-
Organizing Map), and GMM (Gaussian Mixture
Model) have been evaluated in our work.
 FCM is a clustering technique wherein each data
point belongs to a cluster to some degree that is
specified by a membership degree.
 However, K-means assigns each point to the cluster
whose center (centroid) is nearest.
– Euclidean distance is used in our work because of its better
performance than city-block and Hamming distance criteria.
– Also it is faster that Mahalanobis distance.

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Clustering algorithms
 The other clustering algorithm that we have
utilized is SOM neural network.
 It is an unsupervised competitive neural
network.
 The structure of the neural network is as
follows:

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Clustering algorithms
 Finally, GMM is our last clustering algorithm.
 In GMM, each mass of features is modeled as
multivariate normal density function.
 These models are fit to data using expectation
maximization algorithm, which assign a posteriori
probability to each observation.
 The dependency of each pixel to a specific cluster is
determined by examining the value of the probability.

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Clustering algorithms: fusion
 Choosing a clustering
method depends on the input
data distribution.
– Highly overlapped feature
space  SOM
– Moderately overlapped feature
space  FCM and K-means
– Feature space with suitable
detachability  GMM
 To incorporate these
clustering algorithms, a
fusion of them is used here.

13. 13
Level set
 Unlike previous algorithms, the cluster map
is used to evolve the contour.
 This significantly, decreases the
computational complexity.

14. 14
Simulation results
 Our algorithm has been evaluated on an Intel
Core 2 Due CPU (T7250).
 59 images from Corel texture dataset has been
used.
 The average values for :
 120 and 80 epochs for training the first and the
second SOM stages, respectively.
1543.0,2462.0,3176.0,2819.0 4321 ==== αααα
iα

15. 15
Simulation results
 The input image and the ground
truth
 Color transformation result
 Non-linear diffusion result

16. 16
Simulation results
 The clustering
results

17. 17
Simulation results
 (a) contour
initialization
 (b) 60th
iteration
 (c) 100th
iteration
 (d) The final
segmentation
result

18. 18
Simulation results
 PSNR =
28.22 (db)
 (a) contour
initialization
 (b) 20th
iteration
 (c) 120th
iteration
 (d) The final
segmentation
result

19. 19
Simulation results
 PSNR =
27.63 (db)
 (a) contour
initialization
 (b) 60th
iteration
 (c) 120th
iteration
 (d) The final
segmentation
result

20. 20
Simulation results
 Clustering algorithms performance: PSNR
vs. PCS

21. 21
Simulation results
 Comparison between our algorithm and
traditional level set methods proposed in [1
and 2]

22. 22
Simulation results

23. 23
Simulation results
 PSNR =
28.53 (db)
 (a) The input
image
 (b)
initialization
 (c) 60th
iteration
 (d) The final
segmentation
result

24. 24
Simulation results
 PSNR =
26.72 (db)
 (a) The input
image
 (b)
initialization
 (c) 60th
iteration
 (d) The final
segmentation
result

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Simulation results
 We claimed that CIE Lab color space is
highly more suitable for setting up a feature
space, instead of RGB color space.

26. 26
Simulation results

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Summary
 In this paper, a fast level set based method has been
proposed for image segmentation.
 Our algorithm is robust against noise.
 The proposed feature space has much less
dimensionality compared to Gabor and structure
tensors.
 Unlike [1], image gradients have not been calculated,
which decreases the effects of noise.
 Using fusion, significantly increases the
generalization of the clustering algorithms.

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References
[1] S. Daniel Cremers, M. Rousson, and R. Deriche, "A Review of Statistical Approaches
to level sets Segmentation: Integrating Colour, Texture, Motion and Shape", 2007,
International Journal of Computer Vision 72(2), 195–215
[2] M. Rousson, T. Brox, and R. Deriche, "Active Unsupervised Texture Segmentation on
a Diffusion Based Feature Space", 2003, Proceedings of the 2003 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR’03)

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Thanks for your attention!