An image recognition method based on hierarchical feature extraction and multi-layer impulse neural network

Abstract

The invention discloses an image recognition method based on hierarchical feature extraction and multi-layer impulse neural network. According to the processing mode of the visual information by the visual cortex, on the basis of a HMAX model, a sparse feature and feature autonomous learning method is introduced to make the hierarchical feature extraction result retain valid information reasonably, and a multi-layer impulse neural network model based on STDP and back propagation algorithm is used to realize the training and recognition of extracted data. Moreover, phase coding is used as a bridge between hierarchical feature extraction and multi-layer impulse neural network, which effectively converts pixel information into time information and improves the recognition accuracy. The imagerecognition method of the invention not only satisfies the biological characteristics but also has good classification performance. In the process of hierarchical feature extraction, the combination of manual features and autonomous learning features can better meet different needs; at the same time, using multi-layer impulse neural network to identify and classify the complex data can be effectively processed.

Description

technical field [0001] The invention relates to the field of impulse neural networks, in particular to an image recognition method based on layered feature extraction and multi-layer impulse neural networks. Background technique [0002] At present, a series of computational models have been used to simulate the input-output relationship of visual information in the visual cortical area. These models focus on the representation of information, motion state, color texture, etc. or focus on specific functions, such as object recognition, boundary detection, action recognition, etc. While these models explain the underlying computational mechanisms of vision, they lack biologically grounded explanations. In order to simulate the high-efficiency and low-power characteristics of the visual information processing of the brain's visual cortex, the biological neural network is applied to the computer vision computing model. Based on the biological principles of the visual system, b...

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Problems solved by technology

Using probabilistic methods to solve the problem of discontinuous pulse firing in multi-layer spiking neural networks, which leads to difficult feedback

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