A Method of Image Feature Extraction and Training Based on 3D Convolutional Neural Network

Abstract

An image feature extraction and training method based on a three-dimensional convolutional neural network of the present invention comprises the following steps: step 1, normalizing the size of the input image for feature submission; step 2, constructing a convolutional layer, activation Layer, pooling layer, fully connected layer and output layer of the three-dimensional convolutional neural network; step 3, after training the constructed three-dimensional convolutional neural network, the optimized three-dimensional convolutional neural network is obtained, and feature extraction is performed on the input image, Complete the classification recognition of the input image. The three-dimensional convolutional neural network is used for feature extraction and recognition of three-dimensional images. The three-dimensional convolutional neural network is directly convolved on the three-dimensional image to extract the three-dimensional spatial features of the image, which can more effectively express the feature mode of the three-dimensional image, so as to achieve The purpose of image classification recognition.

Description

technical field [0001] The invention belongs to the field of image recognition and deep learning, relates to feature extraction and recognition of three-dimensional images, in particular to an image feature extraction and training method based on a three-dimensional convolutional neural network. Background technique [0002] Image recognition is a technology in which computers process, analyze and understand images to identify targets and objects in various patterns. It has been applied to various aspects of industrial security, life, education and so on. Image recognition is an important field of artificial intelligence. In order to teach computers to perform image recognition like humans, people have proposed many image recognition methods. The traditional recognition process includes image preprocessing, image segmentation, feature extraction and judgment matching. Therefore, there are a large number of different algorithms in each intermediate step, and each intermedia...

AI Technical Summary

Problems solved by technology

For these three-dimensional images, the current solution is to average all images in a certain dimension to obtain a two-dimensional image, and then use a two-dimensional deep learning algorithm to identify it. This method combines all images in a certain dimension Averaging is performed, so a lot of information is lost, and not all features can be effectively extracted

Another method is to regard a certain dimension as a channel of a two-dimensional convolutional neural network, that is, how many slices the image has in this dimension, then how many channels there are, and then use the same two-dimensional convolutional neural network Algorithm for recognition, although this method seems to have no loss of information, it turns the three-dimensional image into an isolated two-dimensional image, extracts features from the two-dimensional image, and extracts two-dimensional features, without considering the characteristics of the two-dimensional image The relevance in the third dimension, and the amount of calculation is large, so it does not conform to the essence of the three-dimensional image, the information loss in the recognition process is large, and the recognition degree is low

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