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# System prepended metadata

title: Saliency Detection for Content-aware Image Resizing
tags: [Papers]

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---
title: Saliency Detection for Content-aware Image Resizing
tags: Papers
---
# Saliency Detection for Content-aware Image Resizing

2009 16th IEEE International Conference on Image Processing (ICIP)

* Radhakrishna Achanta
* Sabine Su ̈sstrunk

École Polytechnique Fédérale de Lausanne（洛桑聯邦理工學院）,
School of Computer and Communication Sciences（在瑞士）

124 citations（according to IEEE Xplore）

![](https://i.imgur.com/sQTt9Vr.png)

上排：原seam carving作法
下排：本篇論文

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### Recap / Background

Content-aware **image re-targeting** : change image aspect ratios while preserving important features.
* cropping: 沒有content-aware，會把重要的地方切掉
* image content adaptive warping
* seam carving

#### Method of seam carving
原圖 >> 畫出importance map(又叫energy map, saliency map) >> 切掉seam >> 再畫一次importance map >> 切掉seam >>>  ...

Assigning visual importance values is fundamental to all these automatic re-targeting methods.

簡單來說，畫出那張importance map是seam carving的重點

#### 畫map常用的方法：
* L1 norm of grayscale intensity gradient：曼哈頓距離
* L2 norm of grayscale intensity gradient：歐氏距離
* object detectors: face

![](https://i.imgur.com/EQ7685k.png)

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### This paper

#### 主張：
* color information is ignored
* recompute the energy after seams are removed（要一直重複算importance value）
* noise sensitive（原本的算法接近edge detection，所以很容易被noise干擾）

#### 解決辦法：
* Uniformly assign saliency values to entire salient regions, rather than just edges or texture regions.
* This is achieved by **relying on the global contrast of a pixel** rather than local contrast, measured in terms of both color and intensity features

#### Seam Carving review:
1. Define a vertical (horizontal) seam to be an 8-connected path of low energy pixels in the image from top to bottom (left to right) containing one, and only one, pixel in each row (column) of the image.
![](https://i.imgur.com/aJtD8ai.png)
    * I(x,y) is the grayscale intensity image
    * Eg(x,y) is the resulting importance value
    * 對一個pixel取偏導數，代表去取他的gradient。gradient有兩個方向，所以我們先取x方向的偏導數、再取y方向的偏導數，相加就是總梯度方向和大小。
    
1. DP to find global minimum seam

#### Our saliency map:
1. Conduct gaussian blurring (using a 3x3 or 5x5 binomial kernel) --> img (n x m x 3)
2. Calculate average Lab vector --> vec (3x1)
3. Euclidean distance of vec and img
![](https://i.imgur.com/jp1gfhc.png)
    * **Elab(x, y)** is the importance value
    * **Iμ** is the average of all pixels' vectors
    * **In×n(x,y)** is the corresponding image pixel vector value in the Gaussian blurred version of the original image, 
    * **∥∥** is the L2 norm (Euclidean distance in Lab color space)
    * ![](https://i.imgur.com/biqlJDN.png)

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### Lab color space（CIELAB, CIE L * a * b *）

Euclidean distances in this color space are approximately perceptually uniform.

CIE（International Commission on Illumination（國際照明委員會）），關於光照、顏色的國際組織。

由一個亮度channel和兩個color channel組成。
- L*：亮度
- a*：綠色到紅色的分量
- b*：藍色到黃色的分量

Lab是基於人對顏色的感知來設計的（感知均勻的，**perceptual uniform**），也就是，如果數字（L/a/b）變化的幅度一樣，帶給視覺上的變化幅度也差不多。

相較於RGB和CMYK更符合人類視覺，也更容易調整：調亮度就條L，調色彩平衡就調a和b。

無關設備（**device-independent**）：在給定color space中的白點（white point）後，這個color space就能明確的確定各個顏色，與使用的顯示設備沒有關係。

Lab的色域比RGB, CMYK都要大，超過人類視覺範圍

![](https://i.imgur.com/mZBGaBd.png)


#### 數值表示：
- L*越大，亮度越高，0（黑色）～100（白色）
- a*和b*為0時都代表灰色
- a*從負（綠色）～正（紅色）
- b*從負（藍色）～正（黃色）
- 數字範圍：Luminance takes values between 0 and 100, and the other coordinates typically take values between -100 and 100

#### RGB轉CIELAB：
1. 先將RGB的值調整為介於0~1之間
1. 再將色彩空間轉換至CIE XYZ色彩空間，以下為其轉換矩陣：
![](https://i.imgur.com/VrXbUML.png)
1. 再由CIE XYZ色彩空間轉換至CIELAB色彩空間，方法如下：
![](https://i.imgur.com/G8kgtlU.png)
![](https://i.imgur.com/7oxpxFn.png)

#### References:
* Lab color space:
https://blog.csdn.net/gdymind/article/details/82357139
https://cg2010studio.com/2012/10/02/rgb%e8%88%87cielab%e8%89%b2%e5%bd%a9%e7%a9%ba%e9%96%93%e8%bd%89%e6%8f%9b/
https://en.wikipedia.org/wiki/CIELAB_color_space
https://colorspace.r-forge.r-project.org/reference/LAB.html

* Seam Carving:
https://github.com/andrewdcampbell/seam-carving
https://zhuanlan.zhihu.com/p/38974520
https://blog.csdn.net/a6333230/article/details/89886616