Last time I showed you the basics of using the new graph theory functionality in MATLAB R2015b. Today I want to talk about some functions I put on the File Exchange for making graphs from images. Those of you working with graph-based image analysis algorithms might find them useful. I would be very interested in receiving feedback on these functions.

The function imageGraph creates a graph representing the neighbor relationships for every pixel in an image with a specified size. imageGraph3 does the same thing for a 3-D image array.

The function binaryImageGraph creates a graph representing the neighbor relationships for every foreground pixel in a binary image. binaryImageGraph3 creates a graph from a 3-D binary image array.

The function plotImageGraph plots a graph created by imageGraph or binaryImageGraph with the graph nodes arranged on a pixel grid.

Finally, the function adjacentRegionGraph creates a graph from a label matrix defining image regions. The graph represents the region adjacency relationships.

Contents

Example: Image graph with 8-connected pixels

Create a graph for a 480-by-640 image with 8-connected pixels.

g = imageGraph([480 640],8)

g = 

  graph with properties:

    Edges: [1225442x2 table]
    Nodes: [307200x3 table]

Graph nodes contain the x-coordinate, y-coordinate, and linear index of the corresponding image pixels.

g.Nodes(1:5,:)

ans = 

    x    y    PixelIndex
    _    _    __________

    1    1    1         
    1    2    2         
    1    3    3         
    1    4    4         
    1    5    5         

Plot the image graph using plotImageGraph.

plotImageGraph(g)
axis([100 110 220 230])

Example: Image graph with 4-connected pixels

Create a graph for a 480-by-640 image with 4-connected pixels.

g = imageGraph([480 640],4)

g = 

  graph with properties:

    Edges: [613280x2 table]
    Nodes: [307200x3 table]

plotImageGraph(g)
axis([100 110 220 230])

Example: Image graph with special connectivity

Use a 3-by-3 connectivity matrix to create an image graph with 6-connected pixels. Each pixel is connected to its north, northeast, east, south, southwest, and west neighbors.

conn = [0 1 1; 1 1 1; 1 1 0];
g = imageGraph([480 640],conn);
plotImageGraph(g)
axis([100 110 220 230])

Example: Binary image graph

Create a graph whose nodes are the foreground pixels of the binary image text.png (a sample image that is included with the Image Processing Toolbox).

bw = imread('text.png');
imshow(bw)
title('Original image')

g = binaryImageGraph(bw);
figure
plotImageGraph(g)
axis([60 85 30 45])

Region Graphs

A label matrix defines a set of regions based on each unique element value in the matrix. For example, suppose you had the following label matrix:

L = [10 10 3 4.5 4.5; 10 10 3 4.5 4.5; 20 20 3 15 15; 20 20 3 15 15]

L =

   10.0000   10.0000    3.0000    4.5000    4.5000
   10.0000   10.0000    3.0000    4.5000    4.5000
   20.0000   20.0000    3.0000   15.0000   15.0000
   20.0000   20.0000    3.0000   15.0000   15.0000

This label matrix defines 5 regions:

The function adjacentRegionsGraph returns a graph with the same number of nodes as labeled regions. Edges in the graph indicate pairs of adjacent regions.

Example: Region graph

Compute an adjacent regions graph. Plot the graph, highlighting the nodes connected to the label 15.

g = adjacentRegionsGraph(L)

g = 

  graph with properties:

    Edges: [6x2 table]
    Nodes: [5x1 table]

The graph nodes contain the region labels.

g.Nodes

ans = 

    Label
    _____

      3  
    4.5  
     10  
     15  
     20  

The graph edges contain the labels for each adjacent region pair.

g.Edges

ans = 

    EndNodes      Labels  
    ________    __________

    1    2        3    4.5
    1    3        3     10
    1    4        3     15
    1    5        3     20
    2    4      4.5     15
    3    5       10     20

Plot the graph, capturing the GraphPlot object as an output argument.

gp = plot(g,'NodeLabel',g.Nodes.Label)

gp = 

  GraphPlot with properties:

     NodeColor: [0 0.4470 0.7410]
    MarkerSize: 4
        Marker: 'o'
     EdgeColor: [0 0.4470 0.7410]
     LineWidth: 0.5000
     LineStyle: '-'
     NodeLabel: {'3'  '4.5'  '10'  '15'  '20'}
     EdgeLabel: {}
         XData: [0.0011 0.4051 -1.3642 1.3629 -0.4048]
         YData: [0.0015 1.5564 -0.8540 0.8538 -1.5577]

  Use GET to show all properties

Find the neighbors of the region labeled 15.

node_num = find(g.Nodes.Label == 15);
neighbors_15 = neighbors(g,node_num);
highlight(gp,node_num,neighbors_15)

Next time I plan to show you a graph-based maze solver ... that cheats.

I'd love for you try Image Graphs. Please let me know what you think.

Copyright 2015 The MathWorks, Inc.
Get the MATLAB code

Published with MATLAB® R2015b