review

Image segmentation (segmentation, cut) refers to the process of dividing a digital image into multiple image sub-regions.

There are many important applications in practical scenarios

In generalized image segmentation, traditional methods and deep methods have different definitions for segmentation.

Traditional methods: For image region division, the core issues are: inter-regional differences and intra-regional similarities . Essentially a clustering problem
Depth method: for pixel-level classification of images, the core problem is: pixel-by-pixel classification , which is essentially a classification problem

In fact, in the deep method, there is a more subdivided problem definition for semantic segmentation

Semantic segmentation: separating the foreground from the background
Instance segmentation: classify and segment different objects

This course will briefly introduce traditional image segmentation algorithms, focusing more on semantic segmentation problems.

Traditional methods of image segmentation

In the era of traditional methods, the pixels of the image are mainly divided based on elementary image features, which is essentially a process of merging a cluster of pixels.

The main ideas include:

Threshold method: According to different pixel value levels, different methods are divided. The representative method is the histogram method
Marginal method: Region division based on gradient.
Area method: Starting from a pixel, the surrounding pixels are continuously merged into a larger area. Representative methods: region growing, watershed algorithm
Contour method: set an initial contour, define the function value and optimize it to approximate the real contour of the object.
Graph theory method: treat the region as a whole and cut it continuously. Until the cutting does not move (satisfies the threshold).

regional law

region growing algorithm

Choose a pixel as the center point;
Expand outward from the seed, and gradually add the points that meet the conditions to the seed set;
Execute the second step until no new points are added to the set.

watershed algorithm

Find the minimum gray value pixel point, let the threshold grow from the minimum value
During the growth process, if the adjacent threshold pixel is not classified, add the pixel value
The above process until it meets at the boundary, thereby partitioning the pixels

graph theory

Each pixel is regarded as a node, and pixels and pixel connections are regarded as edges.

from skimage import data, segmentation, color
from skimage import graph
from matplotlib import pyplot as plt


img = data.coffee()

plt.imshow(img)

labels1 = segmentation.slic(img, compactness=30, n_segments=400,
                            start_label=1)
out1 = color.label2rgb(labels1, img, kind='avg', bg_label=0)

g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg', bg_label=0)

fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8))

ax[0].imshow(out1)
ax[1].imshow(out2)

for a in ax:
    a.axis('off')

plt.tight_layout()