基于可重叠混淆树的卷积神经网络.

CNN (convolutional neural network) is widely used in image classification tasks. Most existing CNN-based classification models are trained as flat N-way classifiers. However, the difference among different image categories limits the capacity of the classifier. To solve the problem, this paper proposed CT-CNN (confusion tree convolutional neural network) model which combined the CT with CNN. CT-CNN first established a CT to identify the confused categories. Then it embedded the hierarchical structure of CT into CNN model, which leaded the CNN training procedure to pay more attention on strongly confused categories. Experiments on public datasets prove that CT-CNN can overcome the limitation of uneven distribution of classification difficulty between categories of large-scale datasets and achieve better performance on complex large-scale image classification tasks. [ABSTRACT FROM AUTHOR]