Multimedia Information Retrieval Method Based on Semantic Similarity

The conventional method of multimedia information retrieval has problems such as complex operation, error in information query, and low accuracy. A multimedia information retrieval method based on semantic similarity is proposed, and during a preprocessing operation of a document, learn the methods of classical vector space models and replace dictionaries containing keyword entries with ontology library. Replace the document with the concept described in the using documents and its vectors of feature meaning, and extract and absorb the contents of the document meaningfully. To achieve efficient retrieval completion, we semantically classify documents to make preparations for outreach plans and techniques for more effective use of queried semantic vectors. When calculating the similarity of concepts and attributes, the existence relationship between each conceptual instance and the attribute force is determined, and its influencing factors are analyzed according to the conceptual characteristics to realize the whole process of completing semantic retrieval. Finally, multimedia information retrieval is completed based on semantic similarity. Through comparative experiments, the multimedia information retrieval method based on semantic similarity is compared with the traditional method and the method based on protection data analysis, and it is concluded that the method based on semantic similarity has high accuracy and applicability.