Content semantic image analysis and storage method based on intelligent computing of machine learning annotation

With the popularity of computers and the rapid development of various application platforms, the explosive growth of data poses a huge challenge to data analysis and storage. For large-scale image analysis applications, the time delay in storing read data becomes an important issue that constrains this application. The semantic information asymmetry between image application and storage is the root cause of this problem. In view of content semantic analysis, in recent years, intelligent computing has become the main research direction. Among them, machine learning has become a research hot spot because of its offline learning and online generation characteristics. For the semantics of image content, machine learning can complete tasks such as content semantic association, classification, annotation and hash mapping, and provide algorithm support for applying image semantics and improving semantic analysis ability in large-scale environment. Image annotation is an important topic in the semantic analysis of image content. Annotation can establish a classification relationship between image content and semantics. In order to solve the problem of extracting a large amount of data in large-scale image analysis, a content semantic image content analysis and storage scheme based on intelligent computer learning image annotation is proposed. Combined with DSTH work, the program introduces deep learning, visual lexicon and map metadata. Hash semantic metadata supplemental metadata is obtained through deep learning, and semantic metadata is constructed and managed in a hierarchical structure. In addition, according to the characteristics of the graph structure, by improving the PageRank algorithm, the SemRank node ranking algorithm based on Hamming distance is proposed. Experimental results demonstrate the effectiveness and reliability of the algorithm.