CN117274692A - Image classification method based on new genetic programming structure and genetic modification - Google Patents
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Abstract
The invention discloses an image classification method based on a new genetic programming structure and genetic modification, which comprises the following steps: preprocessing a training data set; adopting an evolution learning method to evolve the GP model, and combining a gene modification method to find an optimal GP model; and placing the test data set into the generated optimal GP model, and acquiring the precision of the test data set. According to the invention, an image color feature extraction layer is added into a new GP model to obtain the color moment of each image, so that the extracted image features contain color information, the final model can effectively solve the problem of gray level/color image classification, and a genetic modification process is added into an algorithm process. In the process of gene modification, a gene modification individual may obtain a GP model with better fitness, and if the condition is met, the running time of the program can be shortened; at the same time, the introduction of genetic modification also contributes to the diversity of populations, which has an important role in improving the effectiveness and performance of the algorithm.
Description
Technical Field
The invention belongs to the technical field of computer software, and particularly relates to an image classification method based on a new genetic programming structure and genetic modification.
Background
Genetic programming is an evolutionary learning algorithm that automatically evolves a computational program that solves problems using principles of biological genetics and natural selection. The deep neural network has a fixed network structure, so the fixed structure of the deep neural network has larger limitation for different image classification tasks, and the interpretation of the fixed structure of the deep neural network is not strong. GPs with flexible representations can find the best solution without using domain knowledge and can develop different GP model expressions for different types of tasks (datasets).
Most GP-based image classification methods typically use gray-scale data sets, only a few of which can process color images, and these methods are often implemented by extracting as many non-image color features as possible. It is noted that in these methods, the classification effect for a gray image may not be ideal on a color image. Currently, most existing GP-based methods do not perform well in color image classification. In addition, in the GP-based image classification method, a long time is often required for the process of evolving the GP model. This is because genetic programming algorithms involve searching and optimizing a large number of solution spaces, requiring finding the best solution among many possibilities. Because of the complexity of the image data, especially in color image classification tasks, more time is spent to accommodate the variation of different color channels and features.
Disclosure of Invention
The present invention aims to provide an image classification method based on a new genetic programming structure and genetic modification, so as to solve the problems in the prior art. These problems include the challenges of existing genetic programming-based methods that do not perform well in color image classification and that require long time to evolve GP model procedures.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a new genetic programming-based architecture comprising the following functional layers:
1. pooling layer: performing compressed size processing on an input image;
2. image filtering layer: performing image enhancement, extraction, image texture reconstruction and other treatments on an input image;
3. edge detection layer: identifying a salient portion of the input image;
4. feature extraction layer: extracting different types of information in the input image;
5. color feature extraction layer: extracting color features in an input image;
6. a tie layer: the image features obtained by different treatments are connected in series;
7. classification layer: applying a classifier to the image features to classify the images;
8. decision layer: and voting out a final result according to the prediction vectors output by different classifiers.
Preferably, the variable layer allows the GP model to have multiple pooling layers or image filtering layers to extract image features, so that the present invention can develop more suitable GP model expressions for different types of tasks due to the flexibility of the GP model expressions.
Preferably, for the color feature extraction layer, the color distribution of the color moment extraction image is adopted, and the output is in a vector form of 1×9.
Preferably, for the classification layer, three-fold cross validation is adopted when each classifier is trained, and the average value of the accuracy of three training sets is taken as the final accuracy.
A method of classifying genetically modified images based on a new genetic programming structure, comprising the steps of:
s1, preprocessing a training data set;
s2, evolving a GP model by adopting an evolution learning method, and searching an optimal GP model by combining a gene modification method;
s3, placing the test data set into the generated optimal GP model, and acquiring the precision of the test data set.
Preferably, the preprocessing stage of the training data set includes reducing the image size by 1/4, converting the image size into a numpy array format, and finally storing the data set as a file with a suffix of a npy format.
Preferably, when the optimal GP model stage is found and evolution is started, a population is initialized at first, and fitness evaluation is carried out on individuals in the population; if the individuals meet the set conditions, directly outputting an optimal GP model; otherwise, carrying out a genetic modification strategy, dividing all individuals in a population into two types according to the number of sub-classifiers, firstly carrying out genetic modification of individuals with 2 sub-classifications, selecting two parents from the population, scoring the sub-classifiers of each parent to establish a scoring table, selecting sub-classifiers with high scores to combine to obtain a genetic modification individual, carrying out fitness evaluation on the genetic modification individual, outputting an optimal GP model if the genetic modification individual accords with a set condition, otherwise, judging whether the genetic modification individual with 3 sub-classifiers accords with the set condition; outputting if the set condition is met, otherwise, putting two genetically modified individuals generated in the genetic modification process into the population; copying, crossing and mutating all individuals in the population to generate new individuals, wherein the new individuals and the original population form a next generation population; and evaluating the fitness of the new generation population, and repeating the steps until an optimal GP model meeting the conditions is found.
Preferably, the algorithm has a list for storing the fitness values of all the individuals in the previous generation population, and if the individuals in the next generation population are consistent with the individuals in the previous generation population, the fitness values stored in the list can be directly called.
The invention has the technical effects and advantages that: compared with the prior art, the image classification method based on the novel genetic programming structure and the genetic modification has the following advantages:
1. adding an image color feature extraction layer into the new GP model to obtain the color moment of each image, so that the extracted image features contain color information, and the final model can effectively solve the problem of gray level/color image classification;
2. a genetic modification process is added into the algorithm process, in the genetic modification process, a genetic modification individual can obtain a GP model with better fitness, and if the condition is met, the running time of the program can be shortened; at the same time, the introduction of genetic modification also contributes to the diversity of populations, which has an important role in improving the effectiveness and performance of the algorithm.
Drawings
FIG. 1 is a flow chart of an algorithm of the present invention;
FIG. 2 is a flow chart of the genetic modification process of the present invention;
FIG. 3 is a GP tree model diagram of the present invention
FIG. 4 is a schematic view of a tie layer of the present invention;
FIG. 5 is a schematic diagram of a decision layer according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a novel genetic programming structure as shown in fig. 1-5, which comprises the following functional layers:
1. pooling layer: performing compressed size processing on an input image;
2. image filtering layer: performing image enhancement, extraction, image texture reconstruction and other treatments on an input image;
3. edge detection layer: identifying a salient portion of the input image;
4. feature extraction layer: extracting different types of information in the input image;
5. color feature extraction layer: extracting color features in an input image;
6. a tie layer: the image features obtained by different treatments are connected in series;
7. classification layer: applying a classifier to the image features to classify the images;
8. decision layer: and voting out a final result according to the prediction vectors output by different classifiers.
Preferably, the variable layer allows the GP model to have a plurality of pooling layers or image filtering layers to extract image features, and due to the flexibility of the GP model expression, the invention can develop more suitable GP model expressions for different types of tasks.
Preferably, for the color feature extraction layer, the color distribution of the color moment extraction image is adopted, and the output is in a 1×9 vector form.
Preferably, for the classification layer, three-fold cross validation is adopted when each classifier is trained, and the average value of the accuracy of three training sets is taken as the final accuracy.
A method of classifying genetically modified images based on a new genetic programming structure, comprising the steps of:
s1, preprocessing a training data set;
s2, evolving a GP model by adopting an evolution learning method, and searching an optimal GP model by combining a gene modification method;
s3, placing the test data set into the generated optimal GP model, and acquiring the precision of the test data set.
Preferably, the preprocessing stage of the training data set includes reducing the image size by 1/4, converting the image size into a numpy array format, and finally storing the data set as a file with a suffix of a npy format.
Preferably, when the optimal GP model stage is found and evolution is started, a population is initialized at first, and fitness evaluation is carried out on individuals in the population; if the individuals meet the set conditions, directly outputting an optimal GP model; otherwise, carrying out a genetic modification strategy, dividing all individuals in a population into two types according to the number of sub-classifiers, firstly carrying out genetic modification of individuals with 2 sub-classifications, selecting two parents from the population, scoring the sub-classifiers of each parent to establish a scoring table, selecting sub-classifiers with high scores to combine to obtain a genetic modification individual, carrying out fitness evaluation on the genetic modification individual, outputting an optimal GP model if the genetic modification individual accords with a set condition, otherwise, judging whether the genetic modification individual with 3 sub-classifiers accords with the set condition; outputting if the set condition is met, otherwise, putting two genetically modified individuals generated in the genetic modification process into the population; copying, crossing and mutating all individuals in the population to generate new individuals, wherein the new individuals and the original population form a next generation population; and evaluating the fitness of the new generation population, and repeating the steps until an optimal GP model meeting the conditions is found.
Preferably, the algorithm has a list for storing the fitness values of all the individuals in the previous generation population, and if the individuals in the next generation population are consistent with the individuals in the previous generation population, the fitness values stored in the list can be directly called.
Referring to fig. 1, a global flow chart of the overall algorithm: the training data set is input into GP model individuals in the population, and output is obtained after a series of operations. In the fitness evaluation phase, fitness values of the previous generation population are stored in a list. Individuals in the modern population are searched to see if there are the same individuals as in the previous generation population. If the number exists, the fitness value of the individual in the previous generation population is assigned to the modern individual; otherwise, calculating the output classification accuracy and taking the classification accuracy as the fitness value of the modern individuals. The GP-based system consists of two parts, including GP model program structure (evolution learning/training) and classification (testing) with GP models, note that the average of the accuracy of each part after five-fold intersection is used in fitness evaluation.
Referring to fig. 2, the genetic modification process flow: and when the optimal GP model stage is found and evolution is started, initializing a population at first, and evaluating the fitness of individuals in the population. If the individuals meet the set conditions, the optimal GP model is directly output. Otherwise, carrying out a genetic modification strategy, dividing all individuals in the population into two types according to the number of the sub-classifiers, firstly carrying out genetic modification of individuals with 2 sub-classifications, selecting two parents from the population, scoring the sub-classifiers of each parent to establish a scoring table, selecting sub-classifiers with high scores to combine to obtain a genetic modification individual, carrying out fitness evaluation on the genetic modification individual, outputting an optimal GP model if the genetic modification individual accords with a set condition, otherwise, judging whether the genetic modification individual with 3 sub-classifiers accords with the set condition. Outputting if the set condition is met, otherwise placing two genetically modified individuals generated in the genetic modification process into the population. All individuals in the population are replicated, crossed and mutated to generate new individuals, and the new individuals and the original population form the next generation population. And evaluating the fitness of the new generation population, and repeating the steps until an optimal GP model meeting the conditions is found.
Referring to fig. 3, a new genetic programming tree structure: the solid line block diagram is a fixed layer, and there is only one layer in the GP model; the dashed box is a variable layer, which may not be present in the GP model or may have multiple layers. One GP model is provided with 2 or 3 sub-classifiers, and each sub-classifier is provided with three branches corresponding to three image feature processing branches of a feature extraction layer, an edge detection layer and a color feature extraction layer. The reason why the image filter layer must be first subjected to the feature extraction is that the extracted features may contain noise in the image that is not subjected to the filtering denoising process.
The feature extracted by the branches of the feature extraction layer and the color feature extraction layer are vectors, and the feature extracted by the branches of the edge detection layer is an image parameter, so that the connecting layer needs to perform the operation of the Fcon part in the fourth figure, and each row of the image parameter features is connected in series and then connected in series with the feature vectors; and performing the operation of a sea part by utilizing the feature vector obtained by Fcon and the feature vector extracted by the branches of the color feature extraction layer to form a mixed feature.
GP tree model: the new GP tree structure has many different layers, such as an input layer, a pooling layer, an image filtering layer, a feature extraction layer, an edge detection layer, a color feature extraction layer, a tie layer, a classification layer, a decision layer, and an output layer. The pooling layer can effectively reduce the size of the image; the image filtering layer applies a filtering operation or other operation on the image; the feature extraction layer extracts features from the image by using a plurality of feature extraction methods; the edge detection layer acquires information of an important area in the image; the color feature extraction layer acquires image color distribution; the connecting layer connects the image features obtained by different operations in series; the classifying layer applies a classifier to the image characteristics to classify the images; the decision layer decides the final class of the input image. (the maximum depth of the GP tree is 10).
Referring to fig. 4, the tie layer is illustrated: the features obtained by processing the 3 branches are connected in series, and there are two cases that the input is in the form of a vector, and the input contains both the image parameter form and the vector form. For the first case, directly connecting the features in the form of vectors in series to obtain a vector; for the second case, each row of image parameter features is first concatenated, then concatenated with the feature vector.
Referring to fig. 5, in the classification layer, a method of triple-folded cross-validation is applied, taking the average value of three classification accuracy as the final fraction of the sub-classifier. The decision layer integrates the outputs of the two or three sub-classifiers, and the final classification result of the image is obtained according to the predictive vector voting. And if the obtained category numbers are the same, randomly outputting one of the categories. The decision layer is schematic: and comparing the results of 2/3 predictive vectors, and voting out a large number of results as the final classification category of the image.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (8)
1. A new genetic programming-based structure, comprising the following functional layers:
1. pooling layer: performing compressed size processing on an input image;
2. image filtering layer: performing image enhancement, extraction, image texture reconstruction and other treatments on an input image;
3. edge detection layer: identifying a salient portion of the input image;
4. feature extraction layer: extracting different types of information in the input image;
5. color feature extraction layer: extracting color features in an input image;
6. a tie layer: the image features obtained by different treatments are connected in series;
7. classification layer: applying a classifier to the image features to classify the images;
8. decision layer: and voting out a final result according to the prediction vectors output by different classifiers.
2. The method for classifying images based on new genetic programming structures and genetic modifications according to claim 1, wherein: the variable layer allows the GP model to have a plurality of pooling layers or image filtering layers to extract image features, and due to the flexibility of the GP model expression, the invention can develop more suitable GP model expressions for different types of tasks.
3. The method for classifying images based on new genetic programming structures and genetic modifications according to claim 1, wherein: the color distribution of the color moment extraction image is adopted for the color feature extraction layer, and the color distribution is output in a vector form of 1 multiplied by 9.
4. The method for classifying images based on new genetic programming structures and genetic modifications according to claim 1, wherein: for the classification layer, three-fold cross validation is adopted when each classifier is trained, and the average value of the accuracy of three training sets is taken as the final accuracy.
5. A method of classifying genetically modified images based on a new genetic programming structure according to claim 1, comprising the steps of:
s1, preprocessing a training data set;
s2, evolving a GP model by adopting an evolution learning method, and searching an optimal GP model by combining a gene modification method;
s3, placing the test data set into the generated optimal GP model, and acquiring the precision of the test data set.
6. The method for classifying genetically modified images based on a new genetic programming construct of claim 5, wherein: the preprocessing stage of the training data set comprises the steps of reducing the image size by 1/4, converting the image size into a numpy array format, and finally storing the data set into a file with the suffix of the format of npy.
7. The method for classifying genetically modified images based on a new genetic programming construct of claim 5, wherein: when the evolution starts, firstly initializing a population, and evaluating the fitness of individuals in the population; if the individuals meet the set conditions, directly outputting an optimal GP model; otherwise, carrying out a genetic modification strategy, dividing all individuals in a population into two types according to the number of sub-classifiers, firstly carrying out genetic modification of individuals with 2 sub-classifications, selecting two parents from the population, scoring the sub-classifiers of each parent to establish a scoring table, selecting sub-classifiers with high scores to combine to obtain a genetic modification individual, carrying out fitness evaluation on the genetic modification individual, outputting an optimal GP model if the genetic modification individual accords with a set condition, otherwise, judging whether the genetic modification individual with 3 sub-classifiers accords with the set condition; outputting if the set condition is met, otherwise, putting two genetically modified individuals generated in the genetic modification process into the population; copying, crossing and mutating all individuals in the population to generate new individuals, wherein the new individuals and the original population form a next generation population; and evaluating the fitness of the new generation population, and repeating the steps until an optimal GP model meeting the conditions is found.
8. The method for classifying genetically modified images based on a new genetic programming construct of claim 7, wherein: the algorithm is provided with a list for storing the fitness values of all the individuals in the previous generation population, and if the individuals in the next generation population are consistent with the individuals in the previous generation population, the fitness values stored in the list can be directly called.
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