使用模糊聚类的胶囊网络在图像分类上的研究

Study on Image Classification of Capsule Network Using Fuzzy Clustering

胶囊网络中动态路由的本质就是聚类算法思想的实现。考虑到已有胶囊网络中的聚类方式需要数据满足一定的分布才能达到最好的效果,且图像特征比较复杂,于是将一种普适性更好的模糊聚类算法作为胶囊网络中的特征整合方式,并添加了一个使用信息熵来度量不确定性的激活值,以区分同一级别胶囊层特征的显著性。同时,借鉴特征金字塔网络的思想,将不同胶囊层的特征采样成相同尺度,然后进行融合独立训练。基于Keras框架进行实验,其结果表明,相比原来的胶囊网络,这种具有新型结构的胶囊网络在MNIST和CIFAR-10上有更高的识别准确率。对比实验证明了模糊聚类算法在胶囊网络上的应用潜力,其改善了原胶囊网络中聚类算法存在局限的问题;也证明了对胶囊网络中不同层的特征进行融合,能够获得包含信息更丰富且表达能力更强的特征。

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.