http://ieeexplore.ieee.org/document/5478632/

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

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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

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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

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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

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About image segmentation and its
methods
 Our proposed method:

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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.

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

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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

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

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Simulation results
 The clustering
results

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

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

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

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Simulation results
 Clustering algorithms performance: PSNR
vs. PCS

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

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Simulation results

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

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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.

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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!