The Research On Genetic Programming Techniques For Object Detection

Object detection is the process of automatically finding objects of interest within images. In view of the current capture and a wealth of information stored in electronic form, which will quickly become a useful and challenging machine learning and computer vision tasks. In practical application, such as satellite remote sensing images in the detection of vessels, in the medical X-rays to identify the location of the tumor and identify the type of tumor. In this paper, a new genetic learning algorithm for each of Genetic Programming (Genetic Programming) for object detection and through experiments carried out to try.This article will use the algorithm has nothing to do with the area - Genetic Programming (Genetic programming) to solve the difficulty of the many types of different object detection problem. Genetic Programming is a new type of search optimization methods. It followed in the evolution of biological and genetic process, in compliance with the "survival of the fittest, survival of the fittest" principle, from a set of randomly generated initial feasible solution to start, through reproduction, crossover and mutation, such as genetic manipulation, and gradually approaching the problem of iterative optimal solution.First of all, the characteristics of the text object extraction is used to extract the pixel statistics of the feature extraction window to do a different region. Experiments show that feature extraction science division of the window in the detection of different objects can be more effective to extract the characteristic values of objects to enhance the efficiency of detection of objects.Secondly, the analysis of genetic programming based on objects of three different detection methods: direct detection, object classification detection method, hybrid method. Research to find a way of thinking is not only to detect the effect can be maintained, but also can reduce training / evolutionary time. Hybrid method which is adopted in the training / evolution phase of the use of two training methods, training in the use of the first classification method, the initial training / evolution that can detect objects in the correct initial population and narrow in the second training / evolution the search space. The second training is training in the first population to be further training / evolution; evolution can correctly detect the target object of the optimal genetic programming. Experiments proved that the hybrid method can maintain the basis of detection rates to shorten the training / evolutionary time.Finally, we adopted two different fitness function of the comparison: one is to respond to the size of genetic programming parameters into the fitness function, a fitness function is no response to the size of genetic programming parameters. Experiments show that the fitness function by adding the size and procedures relating to the contents to ensure accurate detection of objects under the premise of reducing the process to achieve the purposes of scale. Reduction in the size of the procedure can reduce the possible evolution of space exploration to increase the detection efficiency of objects.