基于三支决策的二阶段图像分类方法

针对深度学习单一模型不能有效处理不确定性预测结果的问题,文中从三支决策出发,将阴影集理论引入图像分类中,构建两阶段图像分类方法.首先,使用卷积神经网络分类样本,获得隶属度矩阵.然后,使用基于阴影集的样本划分算法处理隶属度矩阵,获得分类结果中存在不确定性的部分,即不确定域,进行延迟决策.最后,使用特征融合技术,将SVM作为分类器进行二次分类,降低分类结果的不确定性,提高分类准确率.在CIFAR-10、Caltech 101数据集上的实验验证文中方法的有效性.

Two-Stage Image Classification Method Based on Three-Way Decisions

A single model cannot handle the uncertainty in prediction results effectively, and therefore, the shadowed sets theory is introduced into image classification from the perspective of three-way decisions and a two-stage image classification method is designed. Firstly, samples are classified by convolutional neural networks to obtain the membership matrix. Then, a sample partitioning algorithm based on shadowed sets is employed to process the membership matrix and consequently the uncertain part of the classification results, the uncertain domain, for delayed decision making is obtained. Finally, feature fusion technique is utilized and SVM is regarded as a classifier for secondary classification to reduce the uncertainty of the classification results and improve the classification accuracy. Experiments on CIFAR-10 and Caltech 101 datasets validate the effectiveness of the proposed method.