Image classification method based on feature correlation of frequency domain direction

Abstract

The invention discloses an image classification method based on the feature correlation of frequency domain direction, which mainly overcomes the shortages that the existing method has high calculation complexity, low classification precision and low robustness on image size variation. The method comprises the following steps of: (1) selecting a texture sample image and classifying the texture sample image into an image data set of training sample and an image data set of test sample; (2) implementing 2 dimensional fast Fourier transformation on the training sample image and dividing frequencydomain direction subband according to the frequency and direction of the Fourier surface to obtain a frequency domain direction feature matrix; (3) calculating and obtaining a correlation pair sequence according to the correlation among subband features of the frequency domain direction feature matrix; (4) using unitary linear regression model for calculating the classification feature parametersof each correlation pair to form a classifier; (5) fitting the frequency domain direction features of the test sample image with the classifier parameters to obtain a classification label of the testsample; and (6) repeating step (5) to obtain the classification labels of all test samples. The i image classification method can be used for classifying the Brodatz texture images and the SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image classification method which can be used for classifying texture images and synthetic aperture radar (SAR) images. Background technique [0002] Image classification is an important branch of pattern recognition. It is an image processing method that distinguishes different types of objects according to their different features reflected in image information. The main research content of image classification is how to properly describe images, extract features that can effectively represent image attributes, propose effective classification and recognition methods, and classify images accurately and efficiently on this basis. [0003] The application fields of image classification mainly include the following aspects: image texture analysis, image content retrieval, target detection and recognition, etc. Among them, image texture analysis and classification is a...

AI Technical Summary

Problems solved by technology

Methods similar to this idea include using wavelet, contourlet and other transformations for feature extraction and correlation analysis, but the common disadvantage of this type of transformation is that the frame is fixed and the transformed subbands are not divided carefully in terms of frequency and direction.

These shortcomings lead to insufficient feature correlation, limited classification performance and insufficient robustness to image size changes, and high computational complexity.

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