Quaternion Convolutional Neural Network for Color Image Classification and Forensics

The convolutional neural network is widely popular for solving the problems of color image feature extraction. However, in the general network, the interrelationship of the color image channels is neglected. Therefore, a novel quaternion convolutional neural network (QCNN) is proposed in this paper, which always treats color triples as a whole to avoid information loss. The original quaternion convolution operation is presented and constructed to fully mix the information of color channels. The quaternion batch normalization and pooling operations are derived and designed in quaternion domain to further ensure the integrity of color information. Meanwhile, the knowledge of the attention mechanism is incorporated to boost the performance of the proposed QCNN. The experiments demonstrate that the proposed model is more efficient than the traditional convolutional neural network and another QCNN with the same structure, and has better performance in color image classification and color image forensics.