Grid-Based Genetic Algorithm Approach to Colour Image Segmentation - - PowerPoint PPT Presentation

Grid-Based Genetic Algorithm Approach to Colour Image Segmentation

Marco Gallotta Keri Woods Supervised by Audrey Mbogho

Image Segmentation

 Identifying and extracting distinct,

homogeneous regions from an image

 Simplifies the image for further processing:

− shape recognition, medical imaging, face detection

Image Segmentation

 Problem: How do we

segment the following?

− Each petal as a

region?

− Stigma as a region? − Group flowers as

single region?

− Segment the

background?

Human Segmentation

 Segmentation

by human candidates

 Results confirm

no single solution

Genetic Algorithms

 Optimisation technique that works on large

search spaces

 Biological evolution

Genetic Algorithms: Chromosome

 Chromosome encodes a potential solution  Contains parameters  The chromosome is optimised using:

− mutation

crossover

Genetic Algorithms: Fitness

 Fitness function evaluates an individual and

assigns a numerical value

 Used to select fittest individuals for next

iteration

 Crucial in producing good results

Grid Computing

 A system that coordinates resources that are

not subject to centralized control

 Dedicated and non-dedicated resources  Multiple organisations pooling their unused

resources

 Lots of computing power

Grid Computing

Problem: Segmentation

 Segmentation of great importance  No general method of image segmentation  Wide variety of images  Parameters need to be tuned to get optimal

results

Problem: Genetic Algorithms

 Segmentation involves much uncertainty  GA cope well with uncertainty  Alter parameters to optimise segmentation

results

Problem: Computating Requirements

 Image segmentation and genetic algorithms

computationally intensive

 Combined VERY computationally expensive  Solution?

− Work harder − Work smarter − Get help

Problem: GA For The Grid

 Genetic algorithms easily parallelisable  Grid supplies “free” computational resources

Problem: Research Existing Techniques

 Edge detection  Histogram thresholding  Watershed  Region based techniques  Clustering techniques  Model based techniques  Many others

Segmentation Method Implemented

 Chose to implement:

− Watershed − Region Growing − Region Merging

Watershed Transformation

 Calculate a gradient magnitude image  Consider this as a topographic surface  Consider dropping water at each pixel and

• bserving where the trickle ends

 Pixels with the same end point form a region

Watershed Transformation

 (a) Example gradient magnitude image  (b) The two regions that are identified

Watershed Transformation: Example

Region Growing

 Start off with small regions and grow them  Each iteration considers all pixels neighbouring

the regions

 Pixel with the minimum δ is added to the region  This continues until all pixels are assigned to a

region

Region Growing

 The above method requires manual seeds  To automate we introduce a threshold T  If the minimum δ exceeds T then a new region

is created

 Start with an arbitrary pixel as the first region

and iterate as above

Region Growing: Example

Region Merging

 Initially each pixel a region  Adjacent regions merged if criteria met  Continue until no regions meet criteria

Merging Criterion

 Merge if fusion factor less than scale parameter  Fusion factor: change in heterogeneity if

regions merged

 Heterogeneity: colour, compactness,

smoothness

 Scale parameter controls size of resulting

regions

Region Merging: Example

Segmentation Results

• Berkeley Segmentation Dataset
• Watershed fastest
• Performance results:

58.059 Region Merging 2.106 Watershed Transformation 71.806 Region Growing Time (seconds) Segmentation

Segmentation Results

Segmentation Results

Segmentation Results

 All successful but different results  Effect of scale parameter on region merging

− Large scale parameter => large regions

Segmentation Results: Effect of Scale Parameter

Segmentation Results

 [Can get some results off website at

http://people.cs.uct.ac.za/~mgallott/honsproj/]

Genetic Algorithm

• Modify parameters of region merging algorithm
• Scale parameter, weights of components of

heterogeneity

GA: Fitness Function

 Drives evolution of chromosomes  Evaluate quality of segmentation  Unsupervised segmentation

− No external information − Properties of image itself

 How much colour within each region varies  Low fitness = good segmentation

GA: Fitness Function

 For each region standard deviation multiplied

by area

 Sum all regions  Add 1  Multiply by number of regions

Genetic Algorithm Results

• Inconclusive
• Sometimes improvement

Genetic Algorithm Results

 GA with segmentation very computationally

intensive

 Unable to explore full potential  Extremely slow  Therefore grid

Parallel Genetic Algorithms

 Two common models:

− master-slave (left) − Island model (right)

Grid Computing + Genetic Algorithms

 With the Grid, communication between nodes

is expensive (“impossible” in a true Grid)

 Even with communication, building a topology

for the Island model is difficult

 All existing research has used the master-slave

model

Grid Model

 Our model uses ideas

from both master-slave and Island models

 Root node (dedicated

resource) stores a super population

 No direct communication

between sub-nodes

Grid Model: Results

 We were heavily restricted in testing and could

• nly test with eight nodes

 Tests showed the communication overhead

had negligible impact as fitness function increased in complexity

 Results were positive when testing on simple

problems

 Unsuccessful at migrating the segmentation

algorithm to the Grid

Conclusion

 Experimented with 3 segmentation algorithms  Selected region merging for our genetic

algorithm solution

 Genetic algorithm provides potential for

improvement but results inconclusive

 Grid computing showed positive results

however limited resources did not allow for thorough testing

Future Work: Grid

 As we only tested on

a small Grid, we never had scalability issues

 Most Grids are very

large and having a single root node is a bottleneck

 The next stage is to

test out a hierarchical model

Future Work: GA

 Watershed with genetic algorithm  Investigate different fitness functions  Genetic programming to evolve fitness function

− Train for each desired application

Questions

?

http://people.cs.uct.ac.za/~mgallott/honsproj/