Image Recognition and Analysis: A Complex Network-Based Approach

The placement and order of image pixels play a significant role in the accuracy of image recognition in current algorithms. Complex networks will significantly reduce the impact of images on classification recognition accuracy when rotation, translation, and scaling occur. Complex networks’ topological invariance has made it clear that using them to analyze image recognition will considerably increase image classification accuracy. However, most studies of complex networks for image classification have focused on individual networks, neglecting the combination of multiple networks. This paper proposes a new complex network classification method that combines complex networks and convolutional neural networks(CNN) to train classification using deep learning. We show that the method has high classification accuracy and distinct network features and compares well with a single complex network approach. In addition, to make the distribution of the degree histogram of the image more uniform and concentrated, the original formula for calculating the power value was optimized to reduce the influence of the radius parameter on the power value.