A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation

A Scale-Based Connected Coherence Tree Algorithm for

Image SegmentationSource: IEEE Transactions on Image Processing, vol. 17, No. 2, February 2008Author: Jundi Ding, Runing Ma, and Songcan ChenImpact Factor: 2.715Speaker: Chun-Chieh Chen (陳俊杰 )Date: 2008/3/18

Outline

• Introduction

• Connected Coherence Tree Algorithm (CCTA)

• Experimental Results

• Conclusions

Introduction

Connected Coherence Tree Algorithm (CCTA) (1/8)

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98 100 102 104 108 90 90 88 89 90 91

92 97 102 106 107 90 90 88 88 90 91

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94 98 100 104 106 88 89 91 89 88 89

Block size : (2k+1) × (2k+1) , k=1

Threshold :

98 100 102

92 97 102

91 94 102

9N

5

9

7

N

NCF

5.0,

5.0,

NCFseednon

NCFseed

97

98 100 102 104 108 90 90 88 89 90 91

92 97 102 106 107 90 90 88 88 90 91

91 94 102 105 107 90 89 65 67 90 91

94 99 103 104 108 88 91 60 65 88 90

93 98 100 103 109 91 88 63 61 88 91

95 99 103 102 109 88 91 90 91 89 90

94 98 100 104 106 88 89 91 89 88 89

98 100 102

92 97 102

91 94 102

Connected Coherence Tree Algorithm (CCTA) (2/8)

Block size : (2k+1) × (2k+1) , k=7 Threshold : 31

Connected Coherence Tree Algorithm (CCTA) (3/8)

98 100 102 104 108 90 90 88 89 90 91

92 97 102 106 107 90 90 88 88 90 91

91 94 102 105 107 90 89 65 67 90 91

94 99 103 104 108 88 91 60 65 88 90

93 98 100 103 109 91 88 63 61 88 91

95 99 103 102 109 88 91 90 91 89 90

94 98 100 104 106 88 89 91 89 88 89

Block size : (2k+1) × (2k+1) , k=1

Threshold : 5

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98 100 102

92 97 102

91 94 102

98 100 102 104

92 97 102 106

91 94 102 105

94 99 103 104

Connected Coherence Tree Algorithm (CCTA) (4/8)

98 100 102 104 108 90 90 88 89 90 91

92 97 102 106 107 90 90 88 88 90 91

91 94 102 105 107 90 89 65 67 90 91

94 99 103 104 108 88 91 60 65 88 90

93 98 100 103 109 91 88 63 61 88 91

95 99 103 102 109 88 91 90 91 89 90

94 98 100 104 106 88 89 91 89 88 89

98 100 102 104 108 90 90 88 89 90 91

92 97 102 106 107 90 90 88 88 90 91

91 94 102 105 107 90 89 65 67 90 91

94 99 103 104 108 88 91 60 65 88 90

93 98 100 103 109 91 88 63 61 88 91

95 99 103 102 109 88 91 90 91 89 90

94 98 100 104 106 88 89 91 89 88 89

98 100 102 104 108 90 90 88 89 90 91

92 97 102 106 107 90 90 88 88 90 91

91 94 102 105 107 90 89 65 67 90 91

94 99 103 104 108 88 91 60 65 88 90

93 98 100 103 109 91 88 63 61 88 91

95 99 103 102 109 88 91 90 91 89 90

94 98 100 104 106 88 89 91 89 88 89

Block size : (2k+1) × (2k+1) , k=1

Threshold : 5

Connected Coherence Tree Algorithm (CCTA) (5/8)

1CCT 2CCT 3CCT

Connected Coherence Tree Algorithm (CCTA) (6/8)

10,7 k 55,7 k

Connected Coherence Tree Algorithm (CCTA) (7/8)

q1 q2 q3

q4 p q5

q6 q7 q8

k=1

9

....)1( 821 qpqpqppMean

Connected Coherence Tree Algorithm (CCTA) (8/8)

Ave(6) = 29.022 Ave(7) = 31.65

Ave(12) = 41.51 Ave(18) =50.343

Experimental Results(1/8)

• Experiments on Synthetic Images

1CCT2CCT 3CCT 4CCT 5CCT 6CCT

rG

7476.37)6(,6 Avek

Experimental Results(2/8)

• Experiments on Synthetic Images

CCT1 CCT2Gr Ncut KMST

227.42)2(,2 Avek

Experimental Results(3/8)

• Experiments on Synthetic Images

CCTA Ncut KMST

5491.25)8(

20

8

Ave

k

887.21)9(

18

9

Ave

k

9211.9)9(

7

9

Ave

k

Experimental Results(4/8)

• Experiments on Natural Image

Experimental Results(5/8)

• Evaluation of experimental comparison– Entropy-based evaluation function E

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Experimental Results(6/8)21 gray images from the Berkeley segmentation datasets

Experimental Results(7/8)

• Evaluation of experimental comparison– Global Consistency Error (GCE) and Local Consistency Error (LCE)

6

1),,( 21 ipSSE

0),,( 12 ipSSE

1S 2S

1),,( 21 ipSSE

1),,( 12 ipSSE

Experimental Results(8/8)

LCE=0.1110GCE=0.1641

LCE=0.0927GCE=0.1384

LCE=0.0867GCE=0.1327

Conclusions

• Our contribution lies in proposing a scale-based CCTA for image segmentation, which satisfies a so-called 3-E property:– Easy to implement,– Effective for semantic segmentation– Efficient in computational cost.