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1
 Abstract
 Purpose
 Method & Materials
 Result
 Conclusion
2
 A regional contrast based saliency extraction
algorithm
 Image segmentation
 Object recognition
 Adaptive compression of images
 Content-aware image resizing
 Image retrieval
 Compared
 State-of-the-art:precision = 75%, recall = 83%
 This study:precision = 90%, recall = 90%
3
 Contrast analysis for extracting high-resolution
 Which separates a large-scale object from its
surroundings.
 Assignment of comparable saliency values to similar
image regions, and can uniformly highlight entire
objects.
 Saliency of a region depends mainly on its contrast to
the nearby regions.
 Saliency maps should be fast and easy to generate.
4
 Bottom-up*
 Fast
 Pre-attentive
 Data driven
 底層特徵屬性
▪ 顏色、梯度、邊緣、邊界
 Top-down
 Slower
 Task dependent
 Goal driven
5
   

II
ikk
i
IIDIS ,
       Nkkkk IIDIIDIIDIS ,,, 21  
     
出現機率
影像內色彩總數
的色彩值像素
jj
kl
jl
n
j
jlk
cf
n
Ic
ccDfcSIS
:
:
:
,
1


6
  色彩空間內距離於兩像素 ***,:, baLIIIID ikik
 CIE 1976
 L*
 lightness of the color
 L* = 0, yields black
 L* = 100, indicates diffuse white
 a*
 between red/magenta and green
 negative values indicate green
 positive values indicate magenta
 b*
 between yellow and blue
 negative values indicate blue
 positive values indicate yellow
7
 彩色空間, n = 2563 = 16,777,216
 僅用亮度, n = 256
 忽略顏色訊息可區別性
 通道顏色量化
 自然影像顏色僅占全部色彩一小部份
 丟棄頻率較小的色彩(覆蓋5%)
 保留高頻色彩(覆蓋95%)
 每個通道色彩量化至12個值, n = 123
8
9
 改善”相似色彩量化為不同值”的缺點
 灰階相近的分配較大的權重
 相似的灰階會有相似的顯著性值
 最佳效果
 直方圖對比: L*a*b 色彩空間
 平滑: RGB色彩空間
 
 
    



m
i
ii cSccDT
Tm
cS
1
,
1
1
'
  cicDT ,
10
 

m
i
iccDT
1
,
4/nm 
11
 區域切割
 Efficient graph-based image segmentation. 2004
 區域顏色距離
 顯著性值
       jcicDjcficfrrD
n
i
n
j
r ,,,,,, 21
1 1
2121
1 2
 

     
  區域像素數量
 
i
rr
ikrik
rw
rrDrwrS
ik
,
12
http://www.cs.brown.edu/~pff/segment/
http://www.cs.brown.edu/~pff/papers/seg-ijcv.pdf 13
 增加空間權重
 臨近區域權重較大
 遠區域權重較小
       
   2
21
2
21
2
2
DistanceEuclidean
4.0,
DistanceEuclidean,
,
,
exp
yyxx
D
D
rrDrrw
rrD
rS
sss
s
iki
rr s
iks
k
ik









 
定為影響越小越大
兩區域重心


14
15
16
 GrabCut
 GrabCut -Interactive Foreground Extraction using
Iterated Graph Cuts. 2004
 初始閥值:recall=95%對應閥值
 膨漲&侵蝕
 Trimap:前景、背景、未知
 最多疊代四次
17
18
 
recall.thanmoreprecisionweight,3.0
1
2
2
2








RP
RP
F
19
20
21
22
23
 平色彩空間的滑操作
 控制量化缺陷
 RC-maps
 改善HC-map,結合空間關係
24
 致力於處理普通的自然景象
 高紋理的圖像場景不一定能達到最佳效果
25
 Region Contrast, RC:區域對比度
 Histogram Contrast, HC:直方圖對比度
26
27

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Global contrast based salient region detection

Editor's Notes

  1. Object recongnition:目標識別 Content-aware image resiziong: 內容感知影像縮放 Image retrieval: 影像檢索
  2. 大範圍的目標和周圍環境分離開 相似區域有相似的顯著性,且可均勻突顯目標 區域顯著性主要依賴鄰近區域 快、簡單
  3. 顯著值, 忽略空間關係,相同色彩有相同顯著值 算每個色彩值的顯著性