基于深度聚类的目标细粒度分类方法北大核心CSCD

为提高光电系统对弱小目标的识别和分类能力,降低算法对硬件平台和数据的依赖,提出一种无监督分类方法?基于目标深度特征聚类的细粒度分类方法。该方法通过轮廓、颜色、对比度等浅层特征提取提示目标,经超分辨处理后,利用卷积神经网络对目标的深层特征进行编码,进一步采用基于注意机制的主成分分析方法进行降维生成表征矩阵,最后利用聚类的方式实现目标细粒度分类。实验验证了基于不同神经网络的深度聚类方法在不同数据集上的分类性能,其中采用ResNet-34聚类方法在CIFAR-10测试集上细粒度分类性能达92.71%,结果表明,基于深度聚类的目标细粒度方法能够取得与强监督学习方法相当的目标分类效果。此外,还可以根据不同簇数和聚类等级的选择实现不同细粒度的分类效果。

Fine-grained target classification method based on deep clustering

To improve the recognition and classification ability of electro-optical system for dim and small targets, and reduce the dependence of algorithms on hardware platforms and data, an unsupervised classification method was proposed, namely the fine-grained classification method based on deep features clustering. Firstly, the targets were suggested by the extraction of shallow features such as contour, color and contrast. Then, after a super-resolution processing, a convolutional neural network was used to encode the deep features of the target. Furthermore, the principal component analysis based on attention mechanism was adopted to generate the characterization matrix. Finally, the clustering method was used to realize the fine-grained target classification. The experiments were set to verify the classification performance of the deep clustering method based on different neural networks on different data sets. The fine-grained classification performance based on ResNet-34 clustering method reached 92.71% on CIFAR-10 test set. The results show that the fine-grained target method based on deep clustering can achieve the same effect as the strong supervised learning method. In addition, the classification effect of different fine granularity can be realized according to the different numbers of cluster and selection of cluster grades.