Abstract
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.
Highlights
Is paper mainly focuses on the application in text resources, such as scientific papers, but our calculation method can be applied to the other fields
The multimedia digital archives are expressed based on the vector space model. e disadvantage is that in the case of the BP neural network algorithm, it needs to be matched with the multimedia digital archives correctly, and satisfactory results can hardly be obtained
The words are used to store multimedia digital documents in the topic words in advance, and a classifier is created for each word . e new multimedia digital archives are processed by each classifier. e words of significance in this multimedia digital archive will be further provided to the multimedia digital archive
Summary
In order to compare the multimedia digital archives and the interest of users, the multimedia digital archives and user interest models are expressed in a consistent manner. Is method is not subject to the limitation of the part-of-speech of the topics defined in advance, where the dimension of the vector is not fixed in general, while a fixed size can be specified. Cn, where n stands for the size of the model, cj stands for the jth area, and the multimedia digital archive d is represented as a vector of conditional probability d 〈p(c1|d), p(c2|d), . It is assumed that all the features of the multimedia digital archive show up independently, and p(d|cj) can be expressed as the product of the conditional probabilities for all the features of the multimedia digital archive as follows: pd | cj pt | cj.
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