Image classification method based on random Fourier feature transformation

Abstract

The invention relates to an image classification method based on random Fourier feature transformation, and belongs to the technical field of image classification. The classification method comprises the steps of 1 preprocessing a training image, obtaining a preprocessed image, wherein preprocessing comprises graying, geometric transformation, image enhancement, image segmentation and image denoising; 2, performing feature extraction on the preprocessed image to obtain image features, and constructing a training set, wherein the image features comprise color features, texture features, algebraic features and transformation features; 3, training similar image features to obtain a new weight vector and a separation distance; and 4, performing preprocessing, feature extraction, random Fourier transform and classification on the to-be-classified image to obtain a classification result. According to the method, the accuracy of small sample and single sample images is high; the time and space complexity is low; the problems of neural network result selection and local minimum value are avoided; and the generalization of high-dimensional and nonlinear classification problems is good.

Description

technical field [0001] The invention relates to an image classification method based on random Fourier feature transformation, belonging to the technical field of image classification. Background technique [0002] Support vector machines (SVM) is a binary classification model, including one-class support vector machines and two-class support vector machines. The traditional SVM model is a linear classifier and the model is a maximum margin linear classifier defined on the feature space. The learning strategy of SVM is interval maximization, which is essentially a solution to a convex quadratic programming problem, which is also equivalent to the problem of minimizing the regularized hinge loss function. [0003] Compared with the two-class support vector machine, the classification model of the image classification method based on random Fourier feature transform is similar to a one-class support vector machine (one-class SVM) model, which only needs one type of data durin...

AI Technical Summary

Problems solved by technology

Compared with the KNN classifier, the SVM classifier is more suitable for processing high-dimensional data, but due to the limitations of its own algorithm, it takes a lot of time to process large batches of image data, and there is no uniform standard for kernel function selection. Judging based on experience, which leads to unsatisfactory accuracy and time complexity of the algorithm

The BPNN algorithm has a strong nonlinear mapping ability and has a high degree of self-learning and self-adaptive ability. The CNN algorithm has a greater advantage in processing high-dimensional data. Both of them have high image classification accuracy, but both have relatively long training time. Long, the training results are difficult to converge to the global minimum and there are shortcomings such as local minimization problems, which lead to high time complexity and computational complexity in the field of image classification

[0007] The purpose of the present invention is to be committed to solving the technical defects of the above-mentioned algorithm in terms of time complexity and algorithm complexity, while ensuring a higher classification accuracy

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