This document proposes a regional contrast based saliency extraction algorithm that separates objects from their surroundings and assigns comparable saliency values within objects. It quantizes color channels to reduce complexity while preserving most frequent colors. Segmentation is performed and saliency values are calculated based on regional color distances and spatial weights. The algorithm achieves state-of-the-art precision and recall rates for extracting salient objects from natural images.

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

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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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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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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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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
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19.  
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20. 20

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24.  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

25.  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

26.  Region Contrast, RC：區域對比度
 Histogram Contrast, HC：直方圖對比度
26

27. 27