Evolutionary Algorithm for Landmark Detection
There are several algorithms for locating landmarks in images such as satellite maps, medical images etc.
Nowadays evolutionary algorithms such as particle swarm optimization are so useful to perform this task. Evolutionary algorithms generally have two phases, training and test.
Algorithm
In the training phase, we try to learn the algorithm to locate landmarks correctly. This phase performs in some iterations and finally in the last iteration we hope to obtain a system that can locate the landmark correctly. In the particle swarm optimization there are some particles that search for the landmark. Each particle uses a specific formula in each iteration to optimize landmark detection.
Optimization
The fundamental particle swarm optimization algorithm used in training phase generally works as follows:
Randomly initialise 100 individuals in the search space in the range [-1,1]
LOOP UNTIL 100 iterations performed OR detection error of gbest is 0%
FOR each particle p
Detection errors at x = 0
FOR each image i in training set
FOR each pixel coordinate c in i
Evaluate x of p on visual features at c
IF evaluation is highest so far for i THEN
Detected position in i = c
IF distance between detected position and marked-up position > 2mm THEN
Detection errors at x = Detection errors at x + 1
Fitness of p at x = 1- ( Detection errors at x /Total no. of images in training set)
IF new fitness of p at x > previous fitness of p at pbest THEN
pbest fitness of p = new fitness of p at x
pbest position of p = x of p
IF new fitness of p at x > previous gbest fitness THEN
gbest fitness = new fitness of p at x
gbest position of p = x of p
FOR each particle p
Calculate v of p
IF magnitude of v > v max THEN
Magnitude of v = v max
Move x of p to next position using v
IF x of p outside [-1,1] range THEN
x of p = -1 or 1 as appropriate
REPEAT
Output gbest of last iteration as trained detector d
References
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