共用题干First Image-recognition Software1. 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 ac-curate and efficient enough to improve large-scale document searches online.The system uses pix- el(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 ap-plied 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 stud-“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 pix-els to improve document search.”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 pa-ges.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.A:using photosB: description of the HTML pageC: current popular search enginesD: document searchE: information in imagesF: machine vision systems

共用题干
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 ac-curate and efficient enough to improve large-scale document searches online.The system uses pix- el(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 ap-plied 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 stud-
“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 pix-els to improve document search.”
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 pa-ges.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.


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

参考解析

解析:本段大意:达特默斯的研究者们和他们的同事们已经设计出了一种人工智能软件。选项F意思是,人工智能软件被设计出来,与第一段意思一致。故选F。


本段大意:应用照片测试并应用于照片的新系统,第一次显示机器学习运算法则用于图像识别和检索是精确的、高效的,足以改进大规模的网上文件检索。本段主要介绍新系统的功能。故本题选A(新系统的功能)。


根据“Yet,surprisingly…”及其后面内容可知,本段主要介绍现有的大众化的搜索引擎所存在的问题。因此选项D(现有搜索引擎所存在的问题)正确。


本段大意:研究者们设计并测试出了一种机器视野系统,可以在计算机没有进行清晰地编程的情况下,从网页上照片的像素中提取语义信息。研究者们选择性能最好的文本检索搜索引擎并进行了调整,使它能够使用依据他们的方法从网页图片中提取出的其他的语义信息。他们发现这能够在精确度上带来30%的改进。因此B(文本检索的改进)正确。


根据第一段“…that uses photos to locate documents on the Intemet with far greater ac-curacy than ever before.”可知,新系统是通过照片来做文件的检索。


根据第二段最后一句可知,新的系统被期待能够提高文件搜索的精确度。故本题选D。


根据第三段的“all existing popular search engines…strip away the information con-tained in the photos…”可知,E项正确。


根据最后一段“…produced a 30 percent improvement in precision over the original search engine…”可知,新系统在文件搜索上比当前通常使用的搜索引擎更高效。

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共用题干First Image-recognition Software1. 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 ac-curate and efficient enough to improve large-scale document searches online.The system uses pix- el(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 ap-plied 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 stud-“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 pix-els to improve document search.”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 pa-ges.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 photosB: description of the HTML pageC: current popular search enginesD: document searchE: information in imagesF: machine vision systems

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