Biologically-inspired image meaning information autonomous extraction method and device

Abstract

The invention discloses a biologically-inspired image meaning information autonomous extraction method and device. The method comprises the following steps of: using an image data set with labels as training samples for training a convolution neural network; clustering weight parameters of a training network, and carrying out convergence on the weight parameters according to the clustering result; and using the weight parameters after the convergence as new parameters of the convolution neural network, using the new network to extract meaning information characteristics of images, and carrying out identification and classification on the images according to the characteristics. According to the invention, the networked expression method of meanings is given, the autonomous learning and extraction of the meaning information are realized by the utilization of the network structure, the characteristic dimensions are substantially reduced without influencing the model effect, and the explanation capability of the model is simultaneously improved.

Description

technical field [0001] The invention belongs to the field of pattern recognition and machine learning, and relates to an image recognition method, in particular to a biologically inspired method for autonomously extracting image semantic information. Background technique [0002] In recent years, artificial neural networks have achieved great success in the field of computer vision, especially the deep learning neural network proposed in 2006, which has achieved remarkable results in many artificial intelligence fields such as image processing, speech recognition, and natural language understanding. attracted extensive attention from academia and industry. [0003] However, compared with biological neural networks, artificial neural networks still have relatively large defects in terms of robustness and generalization ability. For example, when the existing deep learning neural network performs image classification tasks, a large amount of training data is required to adjus...

AI Technical Summary

Problems solved by technology

[0003] However, compared with biological neural networks, artificial neural networks still have relatively large defects in terms of robustness and generalization ability.

For example, when the existing deep learning neural network performs image classification tasks, a large amount of training data is required to adjust the network parameters, the calculation amount is large, the calculation time is long, and the hardware requirements are relatively high; when the object to be classified is disturbed by noise, or When the image semantics are blurred due to the similar structure between the classified objects, the classification accuracy of the neural network will be seriously affected; in addition, it is difficult to clearly explain the output results of the existing deep learning neural network, which greatly limits the learning of the model with use

Images