adaptive segmentation based on a learned quality metric i. frosio 1, e. ratner 2 1 nvidia, usa, 2...
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Adaptive Segmentation Based on a Learned Quality
MetricI. Frosio1, E. Ratner2
1 NVIDIA, USA, 2 Lyrical Labs, USA
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Motivation: good / bad segmentation
SLIC (Achanta, 2012)
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Motivation: good / bad segmentation
GRAPH-CUT (Felzenszwalb, 2004)
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Motivation: good / bad segmentation
ADAPTIVE GRAPH-CUT (our)
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Motivation: good / bad segmentation
> >
? ? ?SLIC (Achanta, 2012) GRAPH-CUT (Felzenszwalb, 2004) ADAPTIVE GRAPH-CUT (our)
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Motivation: good / bad segmentation
Achanta, 2012 (SLIC); Kaufhold, 2004: segmentation algorithms aggregate sets of perceptually similar pixels in an image.
Felzenszwalb, 2004 (graph-cut): a segmentation algorithm should capture perceptually important groupings or regions, which often reflect global aspects of the image.
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Motivation: segmentation & video compression
Segment motion estimationFrame segmentation
Encoding True block and sub-block motion vectors
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Aim #1: use the human factor(aka segmentation quality metric)
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Aim #2: automatic parameter tuning
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Road map
1) Pick a segmentation algorithm…
2) … Learn a quality metric including the human factor (application needs) …
3) … And put them together (autotuning).
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Graph:
Nodes:
Edges:
Weights:
vi
vj
w(vi, vj)=0
w(vi, vj)>0
Graph-cut
w(vi, vj)>>0
Vvi
Evv ji ,
0, ji vvw
EVG ,
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Internal difference:
Graph-cut
Cm
ijCvvm wCIntmji ,max
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Difference between components:
Graph-cut
Cm
ijCvCvnm wCCDifnjmi ,min,
Cn
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Boundary predicate:
Graph-cut
Ck
Cn
nn
mmnm C
kCInt
C
kCIntCCDif ,min,
10 15 12
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Graph-cut
C1
C2
Boundary predicate:
nn
mmnm C
kCInt
C
kCIntCCDif ,min,
15 8 11
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Graph-cut
C1
C2
Boundary predicate:
Observation scale ~ k
nn
mmnm C
kCInt
C
kCIntCCDif ,min,
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Graph-cutK
= 3
K =
100
K =
10,
000
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Road map
1) Pick a segmentation algorithm…
2) … Learn a quality metric including the human factor…
3) … And put them together (autotuning).
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(Weighted) symmetric uncertainty
seg
BGRimg
BGR
BGRBGRBGR SS
segimgIU
,,,,
,,,,,,
,2
segB
segG
segR
segBB
segGG
segRR
w SSS
SUSUSUU
4 bits------------------ = 33%7 bits + 5 bits
Entropy based average
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k vs. Uw vs. quality
160 x 120 image block
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k vs. Uw vs. quality
Training
160 x 120 blocks
320x240 rgb images
K = [1, …, 10,000]
visual inspection & classification
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k vs. Uw vs. quality
Training
160 x 120 blocks
640x480 rgb images
K = [1, …, 10,000]
visual inspection & classification
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Learning the metric
WEUS N
iiWE
iWiN
iiUS
iWi
m
bUkm
m
bUkmbmE
1,2
,
1,2
,
1
log
1
log,
Uw = m log(k) + b 1, iWE
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Road map
1) Pick a segmentation algorithm…
2) … Learn a quality metric including the human factor…
3) … And put them together (autotuning).
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Automatic k selection
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Automatic k selection
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Automatic k selection
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Automatic k selection
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Automatic k selection
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… and adaptivity
k = k(x,y)
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Road map
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Results - Quality
Adaptive graph-cut (ours)
Graph-cut (Felzensswalb, 2004) *
SLIC (Achanta, 2012) *
* Same number of segments forced for each algorithm
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Results
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Results
SLIC
Graph-cut
Adaptive graph-cut
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Results
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Results
SLIC Graph-cut Adaptive graph-cut
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Results: inter-class contrast(the higher the better)
Sum of the contrasts among segments weighted by their areas (Chabrier, 2004)
Ad
ap
tive
gra
ph
-cu
t
Gra
ph
-cu
t
SL
IC
Inter class contrast
0
0.04
0.08
0.12
0.16
0.2
320x240
averagemedian
Ad
ap
tive
gra
ph
-cu
t
Gra
ph
-cu
t
SL
IC
Inter class contrast
00.020.040.060.080.10.120.140.160.18
640x480
averagemedian
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Results: intra-class uniformity(the lower the better)
Sum of the normalized standard deviation for each region (Chabrier, 2004)
Ad
ap
tive
gra
ph
-cu
t
Gra
ph
-cu
t
SL
IC
Intra class uniformity
0
2
4
6
8
10
12
14
320x240
averagemedian
Ad
ap
tive
gra
ph
-cu
t
Gra
ph
-cu
t
SL
IC
Intra class uniformity
051015202530354045
640x480
averagemedian
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Results: contrast - uniformity ratio(the higher the better)
Ad
ap
tive
gra
ph
-cu
t
Gra
ph
-cu
t
SL
IC
1000 * Inter / Intra
0
5
10
15
20
25
30
35
320x240
averagemedian
Ad
ap
tive
gra
ph
-cu
t
Gra
ph
-cu
t
SL
IC
1000 * Inter / Intra
0
2
4
6
8
10
12
14
640x480
averagemedian
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Discussion
LEARNED segmentation quality metric including the HUMAN FACTOR
Iterative method to AUTOMATICALLY and ADAPTIVELY compute the optimal scale parameter
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A more general approach(edge thresholding segmentation in YUV)
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A more general approach(edge thresholding segmentation in YUV)
Openboradcast encoding (x264)
Lyricallabs encoding (adaptive
segmentation)
Show
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A more general approach(edge thresholding segmentation in YUV)
Openboradcast encoding (x264) Lyricallabs encoding (adaptive segmentation)
Show
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Open issues & improvements
Resolution dependency (160x120 blocks)
Learning: the Berkeley Segmentation Dataset
Avoid iterations (see I. Frosio, SPIE EI 2015)
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Questions
? ? ?