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A Briefly Introduction of Region-Based Image Segmentation
Advice Researcher:
丁建均教授 (Jian-Jiun Ding )Presenter:
郭政錦 (Cheng-Jin Kuo)
Digital Image and Signal Processing LaboratoryGraduate Institute of Communication EngineeringNational Taiwan University, Taipei, Taiwan, ROC
台大電信所 數位影像與訊號處理實驗室
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Outline
Introduction Data Clustering Hierarchical Clustering Partitional Clustering (k-means method) Simulation Results Comparison Conclusion and Future Work References
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Introduction
Image Segmentation:
Threshold Technique Edge-Based Segmentation
Watershed Algorithm Method
Region-Based Segmentation Data Clustering Region Growing Region Splitting and Merging
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Introduction
Image Segmentation:
Threshold Technique Edge-Based Segmentation
Watershed Algorithm Method
Region-Based Segmentation Data Clustering Region Growing Region Splitting and Merging
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Data Clustering
Data Clustering:
Goal: 1. To classify the statistical databases
2. To segment the digital images
by a systematical way.
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Data Clustering
Two Kinds of Data Clustering :
Hierarchical Clustering hierarchical agglomerative algorithm hierarchical divisive algorithm
Partitional Clustering K-means method
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Data Clustering
Two Kinds of Data Clustering :
The biggest difference between them: Hierarchical Clustering
The number of clusters is flexible. Partitional Clustering
The number of clusters is assigned before processing.
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Data Clustering
Two Kinds of Data Clustering :
Hierarchical Clustering hierarchical agglomerative algorithm hierarchical divisive algorithm
Partitional Clustering K-means method
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Hierarchical Clustering
Characteristics:Has been widely used in statistical fieldsTree DiagramSimple ConceptUser could change the number of clusters during
the processing time
Which means: the numbers of clustering is flexible
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Hierarchical Clustering
hierarchical agglomerative algorithm :
1. Treat every single point (pixel) as a cluster.
Numbers of cluster: 9
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Hierarchical Clustering
hierarchical agglomerative algorithm :
2. Find out two clusters ,
for that:iC jC
( , ) m in ( ( , )),
x y
i j x yD C C D C C
for
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Hierarchical Clustering
hierarchical agglomerative algorithm :
The Definition of Distance D: Single-Linkage agglomerative algorithm :
Complete-Linkage agglomerative algorithm :( , ) min( ( , )), a , bi j i jD C C d a b for C C
( , ) max( ( , )), a , bi j i jD C C d a b for C C
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Hierarchical Clustering
hierarchical agglomerative algorithm :
3. Agglomerate them together to form a new cluster.
Numbers of cluster: 8
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Hierarchical Clustering
hierarchical agglomerative algorithm :
4. Repeat step1~step3 until the number of cluster satisfies the requirement.
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Hierarchical Clustering
hierarchical agglomerative algorithm :
4. Repeat step1~step3 until the number of cluster satisfies the requirement.
Numbers of cluster: 7
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Hierarchical Clustering
hierarchical agglomerative algorithm :
4. Repeat step1~step3 until the number of cluster satisfies the requirement.
Numbers of cluster: 6
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Hierarchical Clustering
hierarchical agglomerative algorithm :
4. Repeat step1~step3 until the number of cluster satisfies the requirement.
Numbers of cluster: 5
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Hierarchical Clustering
hierarchical agglomerative algorithm :
4. Repeat step1~step3 until the number of cluster satisfies the requirement.
Numbers of cluster: 4
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Hierarchical Clustering
hierarchical agglomerative algorithm :
4. Repeat step1~step3 until the number of cluster satisfies the requirement.
Numbers of cluster: 3
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Hierarchical Clustering
hierarchical agglomerative algorithm :
4. Repeat step1~step3 until the number of cluster satisfies the requirement.
Numbers of cluster: 2
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Hierarchical Clustering
The tree diagram: (an example)
These two tree diagrams are downloaded from http://neural.cs.nthu.edu.tw/jang/ Author: Professor Jyh-Shing Roger Jang
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Hierarchical Clustering
Advantages:Simple:
We can check all the stages by checking out the tree diagram only.
Number of cluster is flexible:
We can change the number of cluster anytime during processing. which means?
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Hierarchical Clustering
Drawback:
Slower compared to partitional clustering:
Not suitable for processing larger database
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Hierarchical Clustering
Two Kinds of Data Clustering :
Hierarchical Clustering hierarchical agglomerative algorithm hierarchical divisive algorithm
Partitional Clustering K-means method
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Hierarchical Clustering
hierarchical division algorithm :
1. Treat the whole database as a cluster.
Numbers of cluster: 1
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Partitional Clustering
Characteristics:
Computational time is short User have to decide the number of clusters before
starting classifying data The concept of centroid One of the famous method: K-means Method
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Partitional Clustering
K-mean algorithm :
1. Decide the number of the final classified result with N.
Numbers of cluster: N
we now assume N=3
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Partitional Clustering
K-mean algorithm :
2. Randomly choose N point for the centroids of cluster.
(N=3)
Numbers of cluster: N
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Partitional Clustering
K-mean algorithm :
3. Find the nearest point for every centroid of cluster. Classify the point into the cluster.
Notice the definition of the nearest!
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Partitional Clustering
K-mean algorithm :
4. Calculate the new centroid of every cluster.
Notice the definition of the centroid!
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Partitional Clustering
K-mean algorithm :
5. Repeat step1~step4 until all the point are classified.
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Partitional Clustering
K-mean algorithm :
5. Repeat step1~step4 until all the point are classified.
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Partitional Clustering
K-mean algorithm :
5. Repeat step1~step4 until all the point are classified.
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Partitional Clustering
K-mean algorithm :
5. Repeat step1~step4 until all the point are classified.
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Partitional Clustering
K-mean algorithm :
5. Repeat step1~step4 until all the point are classified.
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Partitional Clustering
K-mean algorithm :
6. Data clustering completed
N=3
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Partitional Clustering
Advantages:Fast:
Since the N has been decided at beginning, the computational time is short.
Simple:
Since the N has been decided at beginning, every simulation testing will be clear.
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Partitional Clustering
Drawbacks:N is fixed:
What is the best value of N?Choice of initial points influence the resultCircular shape
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Simulation Results
Original Image
N=2 N=3
N=4 N=5
Processed by K-means:
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Comparison
Hierarchical clustering:ReliableBut is time consumingCannot perform on a large database
Partitional clustering:FastBut got the initial problemAnd the circular shape problem
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Conclusion and Future Work
K-means algorithm has been widely used in image segmentation field because of the fast processing speed.
How do we choose the best N? Can we use PSO algorithm to improve it? Other ideas of image segmentation:
Segment the image by the graphical features
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References
R. C. Gonzalez, R. E. Woods, Digital Image Processing 2nd Edition, Prentice Hall, New Jersey 2002.
M. Petrou and P. Bosdogianni, Image Processing the Fundamentals, Wiley, UK, 2004. W. K. Pratt, Digital Image Processing 4nd Edition, John Wiley & Sons, Inc., Los Altos,
California, 2007. S.C. Satapathy, J. V. R. Murthy, R. Prasada, B.N.V.S.S, R. Prasad, P.V.G.D. “A
Comparative Analysis of Unsupervised K-Means, PSO and Self-Organizing PSO for Image Clustering”, Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on Volume: 2 13-15 Dec. 2007, Page(s): 229-237
J. Tilton, “Analysis of Hierarchically Related Image Segmentations”, pre-sented at the IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Greenbelt, MD, 2003.
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