Abstract
This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.
Highlights
As more and more images are captured in electronic form, the need for programs which can find objects of interest in a database of images is increasing
The results presented here are the best results achieved by the neural network (NN) and we believe that the comparison with the genetic programming (GP) approach is a fair one
The GP-based approach achieved the ideal results on the easy images and the coin images, but resulted in some false alarms on the retina images, for the detection of objects in class haem in which the false alarm rate (FAR) was very high and more than a quarter of the real objects in this class were not detected by the evolved genetic program
Summary
As more and more images are captured in electronic form, the need for programs which can find objects of interest in a database of images is increasing. The search parameters used here include the number of individuals in the population (population-size), the maximum depth of the randomly generated programs in the initial population (initial-max-depth), the maximum depth permitted for programs resulting from crossover and mutation operations (max-depth), and the maximum generations the evolutionary process can run (max-generations). It is impossible to set them very large due to the limitations of the hardware and high cost of computation There is another search parameter, the size of the input field (input-size), which decides the size of the moving window in which a genetic program is computed in the program sweeping procedure.
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