The bilinear convolutional neural network(B-CNN) has been widely used in computer vision. B-CNN can capture the linear correlation between different channels by performing the outer product operation on the features of the convolutional layer output

thus enhancing the representative ability of the convolutional network. Since the non-linear relationship between the channels in the feature map is not taken account of

this method cannot make full use of the richer information contained between the channels. In order to solve this problem

this paper proposes a kernelized bilinear convolutional neural network employing the kernel function to effectively capture the non-linear relationship between the channels in the feature map

and further enhancing the representative ability of the convolutional network. In this paper

the method is evaluated on three common fine-grained benchmarks CUB-200-2011

FGVC-Aircraft and Cars. Experiments show that our method is superior to its counterparts on all three benchmarks.