General image classification and identification system and method based on neural network

Abstract

The invention discloses a general image classification and identification system and method based on neural networks. The system comprises a parameter acquisition module, an expert system database, an algorithm generation module and a parameter adjustment module. Based on a neural network design template embedded in the expert system database, a whole set of corresponding algorithm suitable for the types of the neural networks are automatically generated according to specific application of a user, and a parameter space is automatically explored through learning and testing, and parameters are adjusted to get the best results. The design difficulty of neural networks is greatly reduced, and the user does not need very complicated professional background to directly use the neural networks.

Description

technical field [0001] The invention relates to pattern recognition, in particular to a neural network-based general image classification recognition system and method. Background technique [0002] Image Classification has always been a core topic in the field of computer vision. After more than 60 years of development in image recognition technology, methods based on neural networks have developed to a new level in the field of image recognition. However, training the model still requires a lot of manual intervention, especially for complex systems like CNN, how to select appropriate features, how to design convolution kernels, how to design feature aggregation, how many computing layers are needed in the hidden layer, and many times it is Based on experience. Moreover, based on neural networks, especially deep neural networks, the design and use are still very complicated, requiring strong professional knowledge and researchers with a high professional level, which is no...

AI Technical Summary

Problems solved by technology

However, training the model still requires a lot of manual intervention, especially for complex systems like CNN, how to select appropriate features, how to design convolution kernels, how to design feature aggregation, how many computing layers are needed in the hidden layer, and many times it is Based on experience

Moreover, based on neural networks, especially deep neural networks, it is still very complicated in design and use, requires strong professional knowledge, and requires researchers to have a high professional level, which is not conducive to technology popularization

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