Image Recognition and Analysis: A Complex Network-Based Approach

Abstract

In existing image recognition algorithms, the position and sequence of image pixels are key factors that affect the accuracy of image recognition. Therefore, the topological invariance of complex networks has led to the recognition that applying complex networks to image recognition analysis will significantly reduce the impact of images on classification recognition accuracy when rotation, translation, and scaling occur. However, most studies on image classification by complex networks have focused on a single network, lacking dynamic evolution with the networks among them. In this paper, we propose 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.

Prediction Model for Transient NOx Emission of Diesel Engine Based on CNN-LSTM Network

“…The CNN network structure possesses three key characteristics: local connection, weight sharing, and pooling [19]. These properties grant the network a certain level of invariance to translation, scaling, and rotation, enabling it to capture the spatial characteristics of data [20]. Consequently, when confronted with the spatial correlation among diesel engine control parameters, the CNN can effectively extract relevant feature information.…”