Application and Research of Multi-Feature Fusion Tag Propagation Computer Algorithm in Image Search and Matching

Abstract

When analyzing the advantages and disadvantages of common community discovery algorithms, the paper points out that the label propagation algorithm (LPA) has low time complexity, does not need to set the number of communities in advance, and the calculation process is simple. When dealing with large and complex networks, it has high the characteristics of efficiency. However, the algorithm does not consider the similarity of adjacent nodes in the network structure and content in the process of label propagation. Therefore, from the perspective of node similarity, the paper proposes a multi-feature fusion label propagation algorithm. The algorithm first uses the Sim Rank algorithm to calculate the structural similarity of the nodes in the network, and at the same time uses the main body model to obtain the topic distribution of the node content, and calculates the similarity of the topic distribution of different nodes, and finally merges the two similarities to be the label propagated by adjacent nodes, Give the corresponding weight to improve the communication strategy. Experimental comparison shows that this algorithm is better than the traditional label propagation algorithm.