使用模糊聚类的胶囊网络在图像分类上的研究

doi:10.11896/jsjkx.190200315

Abstract

Abstract: The essence of dynamic routing in capsule network is the implementation of clustering algorithm.Considering that the clustering method used in the previous capsule network requires the data to meet certain distributions to achieve the best effect while features of image are complicated,a more universal fuzzy clustering algorithm was taken as the feature integration scheme to replace the old in this paper.And an activation value using information entropy to measure the indeterminacy was added to the model,so as to distinguish the significance of capsule features at the same layer.Meanwhile,drawing on the idea of feature pyramid network,the features of different capsule layers are sampled to the same size to fuse and then are trained independently.Experimental results based on the Keras framework show that the capsule network with new structure has higher recognition accuracy on MNIST and CIFAR-10 than the original capsule network.The contrast experiments prove great potential of fuzzy clustering algorithm applying on capsule network,which alleviates the limitation of the clustering algorithm in the original capsule network.The results also prove that the features of different layers in the capsule network can be fused to be more informative and expressive.

Key words: Capsule network, Feature pyramid network, Fuzzy clustering algorithm , Image classification, Multi-scale feature fusion

CLC Number:

TP391

ZHANG Tian-zhu, ZOU Cheng-ming. Study on Image Classification of Capsule Network Using Fuzzy Clustering[J].Computer Science, 2019, 46(12): 279-285.

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