HU JUNFENG 2015-11-25

Interactive Image Cutout- Lazy Snapping

“Lazy Snapping”, SIGGRAPH 2004Yin Li, Jian Sun, Chi-Keung Tang, Heung-Yeung Shum

Interactive image cutout

Lazy snapping Demo

Grabcut Demo

Image cutout is the technique of removing an object from its background

Interactive image cutout

Lazy snapping Demo

Grabcut Demo

Image cutout is the technique of removing an object from its background

Lazy snapping

Step 1: a quick object marking step Work at a coarse scale Specifies the object of interest by a few marking lines

Step 2: a simple boundary editing step Work at a finer scale Edit the object boundary by simply clicking and

dragging polygon vertices

Object marking

UI design Two groups of lines for the representative parts of

foreground and background

Representative clustering centers K-means method to obtain 64 clusters

for each class

: for foreground

: for background

{ }FnK

{ }BnK

K-means clustering

Iterating the 4 steps below

Seed initialization Assigning elements

Seed updating Assigning again

Object marking

Foreground/background image segmentationA typical graph-cut problem

Intuition:

classifying the pixels into two groups, which has the Similar feature in this group;

each group has the smoothness assumption, a Commonly used prior knowledge

Graph cut image segmentation

An image cutout problem can be posed as a binary labelling problem on a graph G=(V, E)V: the nodes represent all the pixelsE: the edge linking two neighboring pixels (4-neighborhood)

i: the i-th node Background

Foreground

Edge

1 foreground

0 background

{ }

i

i

x

soluton X x

Graph cut image segmentation

Corresponding to above 2 intuitive steps Define the likelihood energy :

Define the prior energy :

Minimize the above two terms simultaneously

1( )iE x

2 ( , )i jE x x

Encoding the cost when the label of node i is xi

The smaller, the better

Encoding the cost when the label of node i and node j is xi and xj

The smaller, the better

Graph cut image segmentation

The likelihood energy

The prior energy

Graph cuts

Min cut == Max flow

Max flow problem

Bottleneck problem

General algorithms: Ford-Fulkerson algorithm, push-relabel maximum flow new algorithm by Boykov, etc

Boundary editing

Boundary as editable polygon First vertex – border pixel with highest curvature Next vertices: furthest boundary pixel from previous

polygon Stop when distance is below some threshold

UI design/Tools Direct vertex editing Overriding brush

Using graph cuts

Experimental results

分组大作业

Project 1 彩色直方图均衡优化 1 人组 时间： 12 月 11号

Project 2 图像分割 2 人组 提交时间： 12 月 11 号Project 3 图像中物体识别 2-3 人组 提交时间： 12 月 23

号Project 4 使用目标均衡化方法对古代绘画色彩还原 2-3

人组， 12 月 23 号