SEARCH FOR MULTIMEDIA INFORMATION BASED ON NEURAL NETWORKS

Abstract

The article considers approaches to the use of neural networks in multimedia information retrieval. The develop ment of methods for multimedia information retrieval is necessary due to the large amount of such information. Tradi tional methods of multimedia information retrieval have a high speed of data processing, but low accuracy due to the inability of semantic search. The use of neural networks allows for semantic search, which increases its accuracy and completeness. Approaches to the use of neural networks at the stages of indexing and retrieval of multimedia infor mation are considered. With the help of a neural network, a multimedia file is analyzed and classified. The result of classifying a file is used to create its textual description - an annotation that is compared to the search query to deter mine relevance. There are many ready-made classification networks that can be used to speed up the process of creat ing a multimedia search system, but it is not possible to create a neural network to classify all real-world objects, so multiple neural networks should be used. Neural networks are also use to build feature vectors for a media file and a search query. Similarity functions, such as cosine of similarity, are applied to constructed vectors to determine the semantic similarity of a query and a media file. In this case, the search query can be both in text form and in the form of the appropriate format of the desired media file. This approach allows to build an optimal neural network for a specific task. Neural networks are used to compare the constructed annotation of a file and a query, which increases the accu racy and completeness of the search, compared to traditional methods, due to the ability of neural networks to take into account the semantic meaning of the text.