Analysis of User Personalized Retrieval of Multimedia Digital Archives Dependent on BP Neural Network Algorithm

This paper briefly introduces the characteristics of content-based multimedia retrieval under the information background, analyzes the implementation process of these technologies in the multimedia archives retrieval system including video and image information of digital archives, and points out th...

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Bibliographic Details
Published in:Advances in multimedia 2021-12, Vol.2021, p.1-7
Main Author: Wang, Zhongke
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Archives & records
Back propagation networks
Classification
Customers
Customization
Digital archives
Digital imaging
Information retrieval
Multimedia
Neural networks
Probability
Probability distribution
Vector space
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Summary:This paper briefly introduces the characteristics of content-based multimedia retrieval under the information background, analyzes the implementation process of these technologies in the multimedia archives retrieval system including video and image information of digital archives, and points out that the content-based multimedia retrieval technology is bound to be organically combined with the traditional text retrieval methods. The information retrieval technologies in the past can only comply with the specific requirements of customers. Due to their characteristics of universality, they can hardly meet the demands of different environments, various purposes, and different times at the same time yet. Researchers have put forward personalized retrieval of multimedia files based on the BP neural network computing. In this way, the interest model of customers can be analyzed based on the characteristics of the different classification areas of users. Subsequently, the corresponding calculations are carried out, and the model is updated accordingly. Through the experiments, it is verified that the probability model put forward in this paper is the optimal solution to express the interest of customers and its changes.
ISSN:1687-5680
1687-5699
DOI:10.1155/2021/2630254