2. This paper presents a new approach for image segmentation by
applying Pillar-K-means algorithm.
This segmentation process includes a new mechanism for
clustering the elements of high-resolution images in order to
improve precision and reduce computation time
The system applies K-means clustering to the image
segmentation after optimized by Pillar Algorithm
This algorithm is able to optimize the K-means clustering for
image segmentation in aspects of precision and computation
time.
3. 3
Segmentation
Divide the image into segments.
Each segment:
– Looks uniform
– Belongs to a single object.
– Have some uniform attributes.
– All the pixel related to it are connected.
4. Main approaches
• Histogram-based segmentation
• Region-based segmentation
– Edge detection
– Region growing
– Region splitting and merging.
• Clustering
– K-means
– C-means
– Pillar-k means
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5. Practical Applications of Image segmentation
Medical Applications
Locate tumors and other pathologies
Measure tissue volumes
Computer guided surgery
Diagnosis
Treatment planning
Study of anatomical structure
Locate objects in satellite images (roads, forests, etc)
Face Recognition
Finger print Recognition , etc
6. Idea:
•Determine the number of clusters
•Find the cluster centers and point-cluster correspondences
to minimize error
Problem: Exhaustive search is too expensive.
Solution: We will use instead an iterative search.
[Recall the ideal quantization procedure.]
Algorithm
– fix cluster centers; allocate points to closest cluster
– fix allocation; compute best cluster centers
K-means
Error function =
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7. Algorithm
The K-means algorithm is an iterative technique that is used to
partition an image into K clusters.
The basic algorithm is:
Pick K cluster centers, either randomly or based on some
heuristic
Assign each pixel in the image to the cluster that minimizes the
variance between the pixel and the cluster center
Re-compute the cluster centers by averaging all of the pixels in
the cluster
Repeat steps 2 and 3 until convergence is attained (e.g. no
pixels change clusters)
8. Example – clustering with K-means using gray-
level and color histograms
(from slides by D.A. forsyth)
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9. C-means
The fuzzy c-means algorithm is very similar to the k-means algorithm:
Choose a number of clusters.
Assign randomly to each point coefficients for being in the clusters.
Repeat until the algorithm has converged (that is, the coefficients'
change between two iterations is no more than , the given sensitivity
threshold) :
Compute the centroid for each cluster, using the formula above.
For each point, compute its coefficients of being in the clusters,
10. The algorithm minimizes intra-cluster variance as well, but has
the same problems as k-means, the minimum is a local
minimum.
And the results depend on the initial choice of weights.
The expectation-maximization algorithm is a more statistically
formalized method which includes some of these ideas:
partial membership in classes. It has better convergence
properties and is in general preferred to fuzzy-c-means.
11. • Pillar K-means
• The image segmentation is important to unify contiguous colors in
the color vector space into representative colors.
• It can improve significantly performance of the information
extraction, such as color, shape, texture, and structure.
• This section describes our approach for image segmentation using
our proposed Pillar algorithm to optimize K-means clustering.
• The image segmentation system pre-proceeds three steps:
Noise removal,
Color space transformation
Dataset normalization.
14. CONCLUSION
In this project, we have presented a new approach for image
segmentation using Pillar-K-means algorithm.
The system applies K-means clustering after optimized by Pillar Algorithm.
This algorithm is able to optimize the K-means clustering for image segmentation in
aspects of precision and computation time.
A series of experiments involving four different color spaces with variance
constraint and execution time were conducted.
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