Title: An online social network image retrieval using deep belief network

Authors: Chao Guo; Hongzheng Dong

Addresses: College of Electrical Engineering and Automation, Luoyang Institute of Science and Technology, Luoyang 471023, China ' College of Electrical Engineering and Automation, Luoyang Institute of Science and Technology, Luoyang 471023, China

Abstract: In order to solve the problems of high retrieval error rate and long retrieval time existing in traditional online social network image retrieval methods, this paper proposes an online social network image retrieval method based on deep belief network. The restricted Boltzmann mechanism is used to build the deep belief network model, and the model is used to extract the image features of online social network. Cosine similarity calculation method is used to estimate the similarity of image feature vector, and online social network image retrieval is carried out according to the results of online social network image tag extraction. Experimental results show that the accuracy of online social network image feature extraction is always above 95%, and the error rate of image retrieval is between 1% and 2%, the average retrieval time of online social network image is 0.69 s, and the practical application effect is better.

Keywords: deep belief network; online social network; image retrieval; cosine similarity; image tags.

DOI: 10.1504/IJWBC.2023.134866

International Journal of Web Based Communities, 2023 Vol.19 No.4, pp.320 - 331

Received: 29 Dec 2021
Accepted: 06 May 2022
Published online: 15 Nov 2023 *

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