Construction of Automatic Matching Recommendation System for Web Page Image Packaging Design Based on Constrained Clustering Algorithm

Abstract

With the rapid development of artificial intelligence technology, computer vision science has also gained new opportunities. As the foundation of computer vision and numerous artificial intelligence applications, image matching technology has received extensive attention from researchers and companies around the world. However, in web design, the research on the image matching system is not mature enough, which results in a series of problems such that the web design is not beautiful enough, and the figures do not conform to the design theme. Therefore, it is the current trend to deeply study the structure of the automatic matching recommendation system for web page image packaging design. The purpose of this paper is to use the constrained clustering algorithm to study how to construct an automatic matching recommendation system for web page image packaging design. This paper first gives a general introduction to the classification of constrained clustering algorithms. Then, the operation mechanism and model establishment of SURF feature description operator, SIFT feature description operator, and ORB feature description operator are described in detail. Then, through experiments, the matching accuracy of the web page image matching system based on the constrained clustering algorithm and the influence of parameter changes are compared with other algorithms. Finally, a comparative experiment is carried out on the image matching effects of the three feature description operators. The matching speed, noise sensitivity, and rotation type experiments are introduced respectively. By constructing the web page image packaging design of the constrained clustering algorithm to automatically match the algorithm model of the recommender system and experimenting with the model, the advantages of the constrained clustering algorithm in the model construction are proved. The experimental results show that the constrained clustering algorithm has higher image matching efficiency and matching accuracy, and the accuracy of image feature extraction is better than other algorithms. However, when the network structure division attribution threshold is $\varphi =0.4$ , the clustering performance of the constrained clustering algorithm is better. Compared with the parameter 100, when the parameter is 500 and 1000, the accuracy of the constrained clustering algorithm can be improved, and the calculation accuracy is increased by 0.317.