Embed Size (px)
DESCRIPTION
A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation. Source: IEEE Transactions on Image Processing, vol. 17, No. 2, February 2008 Author: Jundi Ding, Runing Ma, and Songcan Chen Impact Factor: 2.715 Speaker: Chun-Chieh Chen ( 陳俊杰 ) Date: 2008/3/18. Outline. - PowerPoint PPT Presentation
Citation preview
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)
7
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 :
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
97
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
C
jIjIjr SSSSIH
1
/log/
IHIHE lr
C
jjIjl RHSSIH
1
/
j
j
Vm j
jj S
mL
S
mLRH
j
log
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.
