1. 1

2.  Abstract
 Purpose
 Method & Materials
 Result
 Conclusion
2

3.  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

4.  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

5.  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

6.    
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  色彩空間內距離於兩像素 ***,:, baLIIIID ikik

7.  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

8.  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. 9

10.  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
 
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11. 11

12.  區域切割
 Efficient graph-based image segmentation. 2004
 區域顏色距離
 顯著性值
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12

13. http://www.cs.brown.edu/~pff/segment/
http://www.cs.brown.edu/~pff/papers/seg-ijcv.pdf 13

14.  增加空間權重
 臨近區域權重較大
 遠區域權重較小
       
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定為影響越小越大
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14

15. 15

16. 16

17.  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. 18

19.  
recall.thanmoreprecisionweight,3.0
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19

20. 20

21. 21

22. 22

23. 23