Cross-media retrieval method based on visual features and semantic features



The invention discloses a cross-media retrieval method based on visual features and semantic features based on complicated relation among mass isomerous data of internet. The method mainly includes steps of 1, using a secondary developed distributive web crawler for fetching data of a target data source; 2, directing at different data sources, compiling different templates for template-based information extraction on web pages, performing analysis and noise removal on the data and storing the data into a data base; 3, extracting feature values of images and creating an index, and creating a semantic association map; 4, using an SVM (Support Vector Machine) and a trained model for classifying content; 5, based on the extracted visual features and semantic features, calculating similarity distance between different types of data and analyzing the relevance of the different types of data. By adopting the method, relevance among different types of data can be dug effectively.




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