First Image-recognitions software 1）Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2）The new system, witch was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm（运算法则）for image recognition and retrieval is accurate and efficienti enough to improve large-scale document searches online. The system uses pixel（像素）data in images and potentially video—rather than just text—to locate documents. It learns to recognize the pixels associatedwith a search phrase by studying the results from text-based image search engines. The knowledge gleaned（收集）from those results can then be applied to other photos without tags or captions（图片说明）,making for more accurate document search results. 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 system—a type of artificialintelligence that allows computers to learn without being explicitly programmed—that 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.

The new system does document retrieval by （　）.

A:information in images B:current popular search engines C:using photos D:machine vision systems E:document search F:description of the HTML page

First Image-recognitions software 1）Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2）The new system, witch was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm（运算法则）for image recognition and retrieval is accurate and efficienti enough to improve large-scale document searches online. The system uses pixel（像素）data in images and potentially video—rather than just text—to locate documents. It learns to recognize the pixels associatedwith a search phrase by studying the results from text-based image search engines. The knowledge gleaned（收集）from those results can then be applied to other photos without tags or captions（图片说明）,making for more accurate document search results. 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 system—a type of artificialintelligence that allows computers to learn without being explicitly programmed—that 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.

The new system is expected to improve precision in （　）.

A:information in images B:current popular search engines C:using photos D:machine vision systems E:document search F:description of the HTML page

First Image-recognitions software 1）Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2）The new system, witch was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm（运算法则）for image recognition and retrieval is accurate and efficienti enough to improve large-scale document searches online. The system uses pixel（像素）data in images and potentially video—rather than just text—to locate documents. It learns to recognize the pixels associatedwith a search phrase by studying the results from text-based image search engines. The knowledge gleaned（收集）from those results can then be applied to other photos without tags or captions（图片说明）,making for more accurate document search results. 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 system—a type of artificialintelligence that allows computers to learn without being explicitly programmed—that 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.

When performing document retrieval the existing search engines ignore （　）.

A:information in images B:current popular search engines C:using photos D:machine vision systems E:document search F:description of the HTML page

First Image-recognitions software 1）Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2）The new system, witch was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm（运算法则）for image recognition and retrieval is accurate and efficienti enough to improve large-scale document searches online. The system uses pixel（像素）data in images and potentially video—rather than just text—to locate documents. It learns to recognize the pixels associatedwith a search phrase by studying the results from text-based image search engines. The knowledge gleaned（收集）from those results can then be applied to other photos without tags or captions（图片说明）,making for more accurate document search results. 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 system—a type of artificialintelligence that allows computers to learn without being explicitly programmed—that 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.

The new system was found more effective in document search than the（　）.

A:information in images B:current popular search engines C:using photos D:machine vision systems E:document search F:description of the HTML page

First Image-recognition Software 1.Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2.The new system, which was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm (运算法则) for image recognition and retrieval is accurate and efficient enough to improve large-scale document searches online. The system uses pixel (像素) data in images and potentially video — rather than just text — to locate documents. It learns to recognize the pixels associated with a search phrase by studying the results from text-based image search engines. The knowledge gleaned (收集) from those results can then be applied to other photos without tags or captions (图片说明), making for more accurate document search results. 3."Over the last 30 years," says Associate Professor Lorenzo 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 data set, where nearly every page includes multiple pictures or videos. When a person looks at a Web page, he immediately gets the gist (主旨) of it by looking at the pictures in it. Yet, surprisingly, all existing popular search engines, such as Google or Bing, strip away the information contained in the photos and use exclusively the text of Web 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 system — a type of artificial intelligence that allows computers to learn without being explicitly programmed — that extracts semantic (语义的) information from the pixels of photos in Web pages. This information 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 Web 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 that this produced a 30 percent improvement in precision over the original search engine purely based on text.

The new system does document retrieval by（）

A:using photos B:description of the HTML page C:current popular search engines D:document search E:information in images F:machine vision systems

First Image-recognition Software 1.Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2.The new system, which was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm (运算法则) for image recognition and retrieval is accurate and efficient enough to improve large-scale document searches online. The system uses pixel (像素) data in images and potentially video — rather than just text — to locate documents. It learns to recognize the pixels associated with a search phrase by studying the results from text-based image search engines. The knowledge gleaned (收集) from those results can then be applied to other photos without tags or captions (图片说明), making for more accurate document search results. 3."Over the last 30 years," says Associate Professor Lorenzo 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 data set, where nearly every page includes multiple pictures or videos. When a person looks at a Web page, he immediately gets the gist (主旨) of it by looking at the pictures in it. Yet, surprisingly, all existing popular search engines, such as Google or Bing, strip away the information contained in the photos and use exclusively the text of Web 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 system — a type of artificial intelligence that allows computers to learn without being explicitly programmed — that extracts semantic (语义的) information from the pixels of photos in Web pages. This information 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 Web 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 that this produced a 30 percent improvement in precision over the original search engine purely based on text.

The new system is expected to improve precision in（）

A:using photos B:description of the HTML page C:current popular search engines D:document search E:information in images F:machine vision systems

First Image-recognition Software 1.Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2.The new system, which was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm (运算法则) for image recognition and retrieval is accurate and efficient enough to improve large-scale document searches online. The system uses pixel (像素) data in images and potentially video — rather than just text — to locate documents. It learns to recognize the pixels associated with a search phrase by studying the results from text-based image search engines. The knowledge gleaned (收集) from those results can then be applied to other photos without tags or captions (图片说明), making for more accurate document search results. 3."Over the last 30 years," says Associate Professor Lorenzo 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 data set, where nearly every page includes multiple pictures or videos. When a person looks at a Web page, he immediately gets the gist (主旨) of it by looking at the pictures in it. Yet, surprisingly, all existing popular search engines, such as Google or Bing, strip away the information contained in the photos and use exclusively the text of Web 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 system — a type of artificial intelligence that allows computers to learn without being explicitly programmed — that extracts semantic (语义的) information from the pixels of photos in Web pages. This information 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 Web 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 that this produced a 30 percent improvement in precision over the original search engine purely based on text.

When performing document retrieval the existing search engines ignore（）

A:using photos B:description of the HTML page C:current popular search engines D:document search E:information in images F:machine vision systems

First Image-recognition Software 1.Dartmouth researchers and their colleagues have created an artificial intelligence software that uses photos to locate documents on the Internet with far greater accuracy than ever before. 2.The new system, which was tested on photos and is now being applied to videos, shows for the first time that a machine learning algorithm (运算法则) for image recognition and retrieval is accurate and efficient enough to improve large-scale document searches online. The system uses pixel (像素) data in images and potentially video — rather than just text — to locate documents. It learns to recognize the pixels associated with a search phrase by studying the results from text-based image search engines. The knowledge gleaned (收集) from those results can then be applied to other photos without tags or captions (图片说明), making for more accurate document search results. 3."Over the last 30 years," says Associate Professor Lorenzo 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 data set, where nearly every page includes multiple pictures or videos. When a person looks at a Web page, he immediately gets the gist (主旨) of it by looking at the pictures in it. Yet, surprisingly, all existing popular search engines, such as Google or Bing, strip away the information contained in the photos and use exclusively the text of Web 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 system — a type of artificial intelligence that allows computers to learn without being explicitly programmed — that extracts semantic (语义的) information from the pixels of photos in Web pages. This information 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 Web 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 that this produced a 30 percent improvement in precision over the original search engine purely based on text.

The new system was found more effective in document search than the（）

A:using photos B:description of the HTML page C:current popular search engines D:document search E:information in images F:machine vision systems

______ are web sites that search the web for occurrences of a specified word or phrase.

A:Search engines B:WWW C:Internet D:Java