In the early stages of media information retrieval, the primary focus was on text retrieval, characterized by its simplicity, slowness, and lack of timeliness. With the expansion of multimedia resources, the research of image retrieval, audio retrieval, and video retrieval is also increasing. The primary technique is text retrieval, which is supported by image retrieval, audio retrieval, and video retrieval. Accurate identification allows these methods to more precisely retrieve the needed information, enhancing the precision of media information retrieval technology. Therefore, Figure 1 shows the general architecture of media information retrieval. First of all, the media information retrieval system needs to process the multimedia information in the database, including extracting the descriptive characteristics of the media information, generating structured data expression through quantitative coding, and then establishing the content index between the media information content and structured data. Then, the media information retrieval system processes the retrieval requirements submitted by users, and performs feature extraction, quantitative coding, and other operations on the text, image, audio or video information to be retrieved to generate a standardized content query expression. Finally, the system matches the query expression with the structured data in the database, sorts the index content according to the similarity calculated by the matching model, and outputs the query results that meet the retrieval requirements.
Figure 1. Media Information Retrieval Technology Architecture
Both feature extraction and quantitative coding are to establish the relationship between multimedia information content and structured data expression. Media information retrieval technology can identify each information feature, effectively extract the useful ability between each feature, and effectively combine it with data expression. For different multimedia information content, the feature extraction process is generally different. The main purpose of feature selection is to correctly identify the same type of content and effectively distinguish different content. Quantization coding is the normalization of extracted features. The commonly used word bag model is to normalize different features into several different categories by means of cluster analysis, and each category is equivalent to a word. Then, the multimedia information content can be quantified by the combination of these words. In the content query, the matching model is used to calculate the similarity between quantitative expressions as the basis for the output of search results.