region-based semi-supervised clustering image segmentation

Region-based

Semi-supervised Clustering

Image Segmentation

2011 Seventh International Conference on Natural Computation (ICNC)

Tongfeng Sun, Zihui Ren, Shifei Ding

School of Information and Electrical Engineering

China University of Mining and Technology, China

(DOI: 10.1109/ICNC.2011.6022385)

Presentation by Onur Yılmaz

Outline

Introduction

Theoretical Analysis

Image Segmentation on Real Data

Experimental Comparison

Conclusion

IntroductionImage Segmentation

Clusterings are often used to segment images.

Image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels)

Possible goals:

SimplifyChange representation

IntroductionProblem

K-means and fuzzy C-means (FCM) are

Unsupervised methods,

«Natural separations of data»

may not represent user's preferences

IntroductionWhat is presented in this paper?

Improvement and application of

Semi supervised clustering in image

segmentation

based on spatial information

Theoretical Analysis

Semi-supervised clustering image segmentation is divided

into four steps:

obtain labeled

data

extract features and form feature

vectors

improve semi-

supervised clustering algorithm

merge and annotate regions

according to

manual guides

based on spatial

information design an appropriate

clustering objective

function and segment

image

Theoretical Analysis

Semi-supervised clustering image segmentation is divided

into four steps:

obtain labeled

data

extract features and form feature

vectors

improve semi-

supervised clustering algorithm

merge and annotate regions

according to

manual guides

based on spatial

information design an appropriate

clustering objective

function and segment

image

Theoretical AnalysisManual Guides

Manual guides directly reflect

user’s intentions and affect the

results of the segmentation

The user provides the number of

clusters and labels some pixels.

Labeled pixels have spatial

information features.

Theoretical Analysis

Semi-supervised clustering image segmentation is divided

into four steps:

obtain labeled

data

extract features and form feature

vectors

improve semi-

supervised clustering algorithm

merge and annotate regions

according to

manual guides

based on spatial

information design an appropriate

clustering objective

function and segment

image

Theoretical AnalysisAttribute Extraction

Characteristics of image pixels use two categories:

color features

neighbouring texture features

YUV color space:

Y → luminance

U → chrominance

V → chroma

Gray Matrix

Theoretical Analysis

Semi-supervised clustering image segmentation is divided

into four steps:

obtain labeled

data

extract features and form feature

vectors

improve semi-

supervised clustering algorithm

merge and annotate regions

according to

manual guides

based on spatial

information design an appropriate

clustering objective

function and segment

image

Theoretical AnalysisSemi-supervised Clustering

K-Means

is «natural» segmentation based on

data themselves

doesn’t represent user's preferences.

To overcome the problem,

Semi-supervised clustering considers

user's preferences

Theoretical AnalysisSemi-supervised Clustering

In this paper, constraint-

based semi-supervised

clustering algorithm is

improved with an objective

function.

Theoretical AnalysisSemi-supervised Clustering

Objective Function

Jobj =Total Weighted

Euclidean

Distance

Total Penalty for

Incorrect Labeled

Segment+

Theoretical AnalysisSemi-supervised Clustering

Objective Function

Jobj =Total Weighted

Euclidean

Distance

Total Penalty for

Incorrect Labeled

Segment+

Sum of distances for

segments to cluster

centers

over all clustersm-th element is assumed

to belong to the i -th

clustering

Cluster

center

# of

clusters

Theoretical AnalysisSemi-supervised Clustering

Objective Function

Jobj =Total Weighted

Euclidean

Distance

Total Penalty for

Incorrect Labeled

Segment+

Penalty function

when labeled data

are incorrectly

segmented Incorrectly assignmentfunction (1 or 0)

Penaltycoeff.

Distance difference toassumed cluster center

and original cluster center

Theoretical AnalysisImplementation of Semi-supervised Clustering

Data X = { xm }

Labeled data X’

Number of clusters k

Maximum number of

iterations s

Iteration termination

condition d

Subsets X1 X2 X3 X4 …. XkSemi-supervised

Clustering

INPUTS OUTPUTS

Theoretical AnalysisImplementation of Semi-supervised Clustering

Initialize cluster centers

Classifydata

Recalculate cluster centers

Checkterminationcondition

Step 1 Step 2 Step 3 Step 4

Satisfied

Not satisfied

Theoretical AnalysisImplementation of Semi-supervised Clustering

Initialize cluster centers

Classifydata

Recalculate cluster centers

Checkterminationcondition

Step 1 Step 2 Step 3 Step 4

Extend «labeled data» to adjacent data

Divide extended data to r subsets where

each subset have same label

Check r (found above) and k (number of

clusters in inputs)

Theoretical AnalysisImplementation of Semi-supervised Clustering

Initialize cluster centers

Classifydata

Recalculate cluster centers

Checkterminationcondition

Step 1 Step 2 Step 3 Step 4

If r = k Calculate cluster centers

If r > k Start over and prompt error

If r < k There is a need for (k-r) more clusters!

• Use k-means clustering to find k clusters

• Remove r cluster centers which are the nearest to

already found

• Add (k-r) cluster centers

Theoretical AnalysisImplementation of Semi-supervised Clustering

Initialize cluster centers

Classifydata

Recalculate cluster centers

Checkterminationcondition

Step 1 Step 2 Step 3 Step 4

Place data into

different clusters to

minimize the objective

function

Theoretical AnalysisImplementation of Semi-supervised Clustering

Initialize cluster centers

Classifydata

Recalculate cluster centers

Checkterminationcondition

Step 1 Step 2 Step 3 Step 4

Recalculate cluster

centers with giving

more weight to the

labeled data

Theoretical AnalysisImplementation of Semi-supervised Clustering

Initialize cluster centers

Classifydata

Recalculate cluster centers

Checkterminationcondition

Step 1 Step 2 Step 3 Step 4

Check for

• Maximum number of

iterations

• Change in cluster centers

Theoretical Analysis

Semi-supervised clustering image segmentation is divided

into four steps:

obtain labeled

data

extract features and form feature

vectors

improve semi-

supervised clustering algorithm

merge and annotate regions

according to

manual guides

based on spatial

information design an appropriate

clustering objective

function and segment

image

Theoretical AnalysisMerging and Annotation

In order to obtain desired objects,

Region merging:

A complex object with several independent segmentations whichshould be merged finally.

Region annotation:

Annotate regions with different meanings based on labeled data,

such as segmentation objects, background, etc.

Morphological dilation and erosion

Image Segmentation on Real DataData Preprocessing Step

200 images:

animals, plants, landscape and other aspects

Image Segmentation on Real DataManual Guides Step

Setting some parameters:

number of cluster classes, initial cluster centers etc.

Parameters should be designated according to

user’s preferences.

river

snow

field catsbackground Field

and 7 more

unlabeled

FLEXIBLE

Image Segmentation on Real DataImage Segmentation Step

Labeled data point (cluster centers from 1 to k)

Image Segmentation on Real DataImage Segmentation Step

Image segmentation results

Image Segmentation on Real DataMerging and Annotation Step

After merging and annotation

Experimental ComparisonExperiment Results

Method Number of iteration Time Accuracy Rate

Semi-supervised

Clustering

7 1.5 s 92 %

K-means Clustering 27 5.3 s 74 %

FCM Clustering 30 6.5 s 78 %

Experimental ComparisonExperiment Results

Semi-supervised clustering greatly

Reduces the number of iteration and

Improves segmentation accuracy

Experimental ComparisonExperiment Results

The weight of labeled data ranging from 1 to 200 plays greater impact on convergence speed

Experimental ComparisonExperiment Results

Semi-supervised clustering has good reliability.

The more labeled clusters are, the more consistent results

Experimental ComparisonExperiment Results

If labeled data are few, semi-supervised clustering is nearly

equivalent to K-means.

In addition, the segmentation results are insensitive to noise

Conclusion

Image segmentation experiments

in different conditions show that

region-based semi-supervised

clustering can improve the

accuracy and speed of

segmentation.

Conclusion

Different weights to labeled data and

unlabeled data in computing cluster

center

penalty function can effectively increases

the influences of manual guides

So the segmentation may be more in line

with user’s requirements.

What is presented?

Introduction

Theoretical Analysis

Image Segmentation on Real Data

Experimental Comparison

Thank you for your interest!

References

M. Mignotte. "A de-texturing and spatially constrained K-means approach for image segmentation," Pattern Recognition Letters. 32 (2), Jan. 2011, pp. 359-367.

R. J. He, B. R. Sajja, S. Datta and P. A. Narayana. "Volume and shape in feature space on adaptive FCM in MRI segmentation," Annals of Biomedical Engineering, 2008, 36(9), pp. 1580-1593.

J. Jin and D. Zhang. "Semi-supervised robust on-line clustering algorithm," Journal of Computer Research and Development, 2008, 45(3), pp. 496-502. (Pubitemid 351648839)

X. Bao, X. Peng, Y. Wang and Z. Cao. "Textile image segmentation based on semi-supervised clustering and Bayes decision," 2009 International Conference on Artificial Intelligence and Computational Intelligence, IEEE Computer Society, Nov. 2009, (3), pp. 559-562.

K. Wagstaf and C. Cardie. "Clustering with instance-level constraints," the 17th International Conference on Machine Learning(ICML), Morgan Kaufmann Publishers Inc, 2000, pp. 1103-1110.

References

E. P. Xing, A. Y. Ng, M. I. Jordan and S. Russell. "Distance metric learning with application to clustering with side-information," In S. Thrun S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, MIT Press, 2003, pp. 505-512.

M. Bleinko, S. Basu and R. J. Mooney. "Integrating constraints and metric learning in semi-supervise clustering," Proceedings of the 21st International Conference on Machine Learning, ACM Press, 2004, pp. 81-88. (Pubitemid 40290795)

L. Vincent. "Graphs and mathematical morphology," Signal Processing, 1989, 16(4), pp. 365-388.

A. Baraldi and F. Parmiggaani. "An investigation of the textural characteristics association with gray level co-occurrence matrix statistical parameters," IEEE Transaction on Geoscience and Remote Sensing, 1995, 33 (2), pp. 293-304.

J. MacQueen. "Some methods for classification and analysis of multivariate observations," In Proceedings of 5th Berkeley Symposiumon Mathematical Statistics and Probability, University of California Press, 1967, pp. 281-297.

Baiwan gallery. http://www.mypcera.com/PHOTO/index.htm. 1 January, 2011.

Region-based

Semi-supervised Clustering

Image Segmentation

2011 Seventh International Conference on Natural Computation (ICNC)

Tongfeng Sun, Zihui Ren, Shifei Ding

School of Information and Electrical Engineering

China University of Mining and Technology, China

(DOI: 10.1109/ICNC.2011.6022385)

Presentation by Onur Yılmaz