Image classification method based on an observation matrix transformation dimension

Abstract

The invention discloses an image classification method based on an observation matrix transformation dimension, which comprises the following steps of: performing sparse coding on an image by using perceptual compression to obtain a data set consisting of low-dimension images, and dividing the data set containing label labels into a training set and a test set; Constructing an image classificationnetwork comprising an input layer, a hidden layer and an output layer, wherein the hidden layer is a perceptron unit; providing At least two image classification networks,wherein each image classification network comprises different node number perceptron units; Taking the training set as input, and carrying out training under the supervision of a label to obtain a corresponding neural network image classification model after the training is completed; Verifying the image classification accuracy of the neural network image classification model by using the test set, and selecting the neural network image classification model with the highest accuracy as a final neural network image classification model; And inputting an image to be detected, and outputting a prediction probability of an image classification result. According to the image classification method provided by the invention, the model efficiency can be greatly improved under the condition that the image classification precision is not reduced.

Description

technical field [0001] The invention belongs to the technical field of image classification, in particular to an image classification method based on transformation dimension of observation matrix. Background technique [0002] Image classification is an image processing method that distinguishes different types of objects according to the different characteristics reflected in the image information. It uses computer to carry out quantitative analysis on images, and classifies each pixel or area in the image or image into one of several categories to replace human visual interpretation. [0003] Classification method of image space—use image grayscale, color, texture, shape, position and other underlying features to classify images; for example: [1] use gray histogram features to classify images; [2] use texture features to classify images Image classification; [3] uses texture, edge and color histogram mixed features to classify images; [1], [2], [3] all use SVM as a class...

AI Technical Summary

Problems solved by technology

After full sampling, the data volume of the digital signal is relatively large, which is not conducive to storage and transmission on the one hand, and on the other hand, the digital signal itself has a lot of redundancy

At present, in the field of image classification, with the increase of input data and model complexity, the amount of calculation is increasing day by day, and the efficiency of model tuning training and use is not high.

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