In image classification, shallow convolutional features and deep convolutional features are not fully utilized by many network frameworks. To solve this problem, we propose a combinatorial convolutional network (CCNet) that integrates convolutional features of all levels. According to its own structure, the convolutional features of shallow, medium, and deep levels are extracted. These features are combined by weighted concatenation and convolutional fusion, and the coefficients of each channel of final combination feature are again weighted to improve the identification degree of features. CCNet can improve the single case where most network only add or concatenate shallow and deep features, so that the network can achieve lower classification error rate while generating low-dimensional features. Extensive experiments are performed on CIFAR-10 and CIFAR-100 respectively. The experimental results show that the low-dimensional image feature vectors generated by CCNet effectively reduce the classification error rate when the number of convolutional layers does not exceed 100 layers.
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This work is supported by the National Natural Science Foundation of China (no. 51641609), Natural Science Foundation of Hebei Province of China (no. F2015203212).
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Wu, C., Li, Y., Zhao, Z. et al. Research on image classification method of features of combinatorial convolution. J Ambient Intell Human Comput 11, 2913–2923 (2020). https://doi.org/10.1007/s12652-019-01433-9
- Image classification
- Convolutional neural network
- Combinatorial convolution
- Weighted concatenation