3)Over the last 30 years,says Associate Professor Korenzo Torresani,a co-author of the study,the web has evolved from a small collection of mostly text documents to a modern,massive,fast-growing multimedia datastet,where nearly every page includes multiple pictures of videos.When a person looks at a Web page,he immediately get the gist(主旨)of it by looking at the pictures in it.Yet,sruprisingly,all existing popular search engine,such as Google or Bing,strip away the information contained in the photos and use exclusively the text of Wed pages to perform the document retrieval.Our study is the first to show that modern machine vision systems are accurate and efficient enough to make effective use of the information contained in image pixels to improve document search
4)The researchers designed and tested a machine vision systema type of artificialintelligence that allows computers to learn without being explicitly programmedthat extracts semantic(语义的)information from pixels of photos in Web pages.This informationg is used to enrich the description of the HTML page used by search engines for document retrieval.The researchers tested their approach using more than 600 search queries(查询)on a database of 50 million Wed pages.They selected the text-retrieval search engine with the best performance and modified it to make use of the additional semantic information extracted by their method from the pictures of the Web pages.They found tht this produced a 30 percent improvement in precision over the original search engine purely based on text.
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