卷积神经网络（CNN）训练中卷积核初始化方法研究

本文提出了一种基于样本图像局部模式聚类的卷积核初始化方法，该方法可用于卷积神经网络（Convolutional neural network, CNN）训练中卷积核的初始化。在卷积神经网络中，卷积核的主要作用可看成是利用匹配滤波提取图像中的局部模式，并将其作为后续图像目标识别的特征。为此本文在图像训练集中选取一部分典型的样本图像，在这些图像中抽取与卷积核相同大小的子图作为图像局部模式矢量集合。首先对局部模式子图集合应用拓扑特性进行粗分类，然后对粗分类后的每一子类采用势函数聚类的方法获取样本图像中的典型局部模式子图，构成候选子图模式集，用它们作为CNN的初始卷积核进行训练。实验结果表明，本文方法可以明显加速CNN网络训练初期的收敛速度，同时对最终训练后的网络识别精度也有一定程度的提高。 

Research on Convolution Kernel Initialization Method in Convolutional Neural Network (CNN) Training

This paper proposes a convolution kernel initialization method based on sample image local pattern clustering, which can be used to initialize the convolution kernel in Convolutional Neural Network training. In convolutional neural networks, the main role of convolution kernels can be seen as the use of matched filtering to extract local patterns in an image and use it as a feature for subsequent image object recognition. To this end, a part of typical sample images are selected in the image training set, and subgraphs of the same size as the convolution kernel are extracted from these images as image local pattern vector sets. Firstly, the topological characteristics of the local pattern subgraph set are applied to the rough classification. Then, for each subclass after the rough classification, the potential local pattern subgraph is obtained by using the potential function clustering method to form the candidate subgraph pattern set. They are trained as the initial convolution kernel of CNN. The experimental results show that the proposed method can significantly accelerate the convergence speed of the initial CNN network training, and also improve the network recognition accuracy after the final training.