Method Of Texture Image Recognition Based On Genetic Programming

Texture is the natural attribute of bark surface, can be used as an important basis for indentifing different species. The classification of plant species extends classification. Plant classification is a foundational work, in agricultural production and application of the forestry statistical very extensive. The traditional classification mainly rely on professional personnel manual operation, low efficiency, and not only to identify the professional quality of professional staff the demand is higher, caused a tremendous human waste of resources. How to seek an effective, can realize the automatic classification of plant, became this field method research hotspot problem. At present, the automatic classification in plants at home and abroad of recognition had some achievements, still need to continuously explore new classification methods, and further increase classification accuracy.This article will use Genetic Programming (GP) - Genetic, to solve the bark texture image recognition problem, the basic idea of Genetic Programming from natural biological evolution process and evolutionary methods, through evolution operation, get the optimal solution. This paper expounds the principle of genetic programming; Analyses the characteristics of genetic programming; The use of genetic programming problem solving bark texture image recognition method, mainly made the following areas:First of all, the extraction of texture eigenvalue method is analyzed, the texture analysis methods including statistics, geometric method, the model method and signal processing method, etc, and based on the natural texture images of bark, determines the statistical analysis and the signal processing method by the two methods of image texture feature extraction bark value. Experimental proof, effectively extract image texture feature of value, can improve the accuracy of plant identification.Thus, fewer image features could be used for good output of image recognition or classification with genetic programming.Secondly, the use of statistical methods and signal processing method of image texture feature extraction bark value. Statistics is tectonic graylevel histogram and gray symbiotic matrix extraction image texture feature of value, and using the signal processing method is calculation method of extraction bark Gabor texture of texture image of eigenvalues. The research idea is extracted in tectonic graylevel histogram texture characteristics of value, the experiment USES 15 kinds of images obtained histogram and sample the 4 second histogram statistic: including mean, variance, the kurtosis and partial degrees, through calculating the 4 second statistics to sample the worth of value; image texture characteristics When in tectonic graylevel co-occurrence matrix, determines the four different directions theta, texture feature parameters take four of the mean and variance direction, and obtain graylevel co-occurrence matrix five texture feature parameters; and put forward and direction irrelevant graylevel co-occurrence matrix characteristics extracted method, i.e. take four directions; the mean and variance; make the application becomes flexible;Gabor texture law is through calculation of the mean and standard deviation bark image as texture eigenvalues.Finally, the use of statistical methods and signal processing method of image texture feature extraction bark dynamic and static class in the value of the border of genetic programming classifier recognition effect. Experiments have proved that using graylevel co-occurrence matrix texture extraction extracted texture feature value in the genetic programming classifier the classification results meets ideal situation. The texture characteristics analysis method of image eigenvalue and extracted with genetic programming classifier for image recognition can significantly improve identification accuracy.