An Image Classification Method Based on Dimension Transformation of Observation Matrix

Abstract

The invention discloses an image classification method based on transforming dimensions of an observation matrix, which includes: using perceptual compression to sparsely encode images to obtain a data set composed of low-dimensional images, and dividing the data set containing labels into a training set and a test set Constructing an image classification network comprising an input layer, a hidden layer and an output layer, the hidden layer is a perceptron unit; the image classification network is at least two, respectively comprising different node number perceptron units; the training set is used as Input, train under the supervision of the label, and obtain the corresponding neural network image classification model after training; use the test set to verify the accuracy of the neural network image classification model for image classification, and select the one with the highest accuracy as the final neural network image classification Model; input the image to be tested, and output the predicted probability of the image classification result. The image classification method provided by the present invention can greatly improve model efficiency without reducing image classification accuracy.

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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