基于聚类和PEDCC-Loss的CNN分类器的分类性能提升  

An Improved Classification Method of CNN Classifier Based on Clustering and PEDCC-Loss

作　　者：赵璐 何子况 朱秋煜[1] Zhao Lu

机构地区：[1]上海大学通信与信息工程学院,上海200444

出　　处：《工业控制计算机》2020年第4期40-41,45,共3页Industrial Control Computer

摘　　要：提出了一种基于PEDCC-Loss和聚类的方法来提升CNN分类器分类性能的算法。利用CNN以及PEDCC-Loss来对图像进行特征提取,然后用BIRCH聚类算法对每类图像的隐特征进行聚类,以获得更好、更逼真的非线性边界,最大程度地减少误分类的边界点。将网络最后一层的PEDCC权重作为每类图像的中心,并以聚类后的子簇的簇心作为分类的辅助判断依据进行图像分类。实验结果表明,该算法的分类准确率相比CNN有一定的提升。This paper proposes an algorithm based on PEDCC-Loss and clustering to improve the classification performance of CNN classifiers.The features are extracted by convolutional neural network and PEDCC-Loss loss function.PEDCC(Predefined Evenly-Distributed Class Centroids)artificially specifies multiple evenly distributed class centroids,which can reach more compact intra-classes distance and more discrete inter-class distance.Then the latent features of each class of image are separately clustered by BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies)clustering algorithm for obtaining a better and more realistic nonlinear boundary to minimize the boundary points of misclassification.The PEDCC weight of the last layer of the network is taken as the center of each type of image,and the centers of the clustered sub-clusters are used for the auxiliary judgment of classification.

关 键 词：CNN 分类器 聚类 PEDCC-Loss BIRCH 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]