Research on Image Classification Combining Wavelet Analysis and Spiking Neural Network

Xiao Fei, Liao Jianping, Tian Jie, Wang Guangshuo
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Abstract

Wavelet analysis is a variant of Fourier analysis, which can be used for time-frequency analysis of signal processing, and has achieved remarkable results in the field of image processing. The spike neural network is called the third-generation neural network, which is different from the previous generation, the neural network of the spike neural network is more inspired by neuroscience, and this neural network is constructed in a way closer to the human brain mechanism, which can be applied to many machine learning tasks. Image classification is one of the basic tasks in the field of computer vision. We explore the application of wavelet analysis to the training process of the spiking neural network, before the original data is input into the neural network, we process it with wavelet transform, so that the characteristics of the input data are easier to be learned by the neural network.
结合小波分析和峰值神经网络的图像分类研究
小波分析是傅里叶分析的一种变体,可用于信号处理的时频分析,在图像处理领域取得了显著的成果。尖峰神经网络被称为第三代神经网络,与上一代不同的是,尖峰神经网络的神经网络更多地受到神经科学的启发,这种神经网络的构建方式更接近于人脑机制,可以应用于许多机器学习任务。图像分类是计算机视觉领域的基本任务之一。我们探索了小波分析在尖峰神经网络训练过程中的应用,在将原始数据输入神经网络之前,对其进行小波变换处理,使输入数据的特征更容易被神经网络学习。
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