MISC

2012年

Understanding and Predicting Importance in Images

2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
  • Alexander C. Berg
  • ,
  • Tamara L. Berg
  • ,
  • Hal Daume
  • ,
  • Jesse Dodge
  • ,
  • Amit Goyal
  • ,
  • Xufeng Han
  • ,
  • Alyssa Mensch
  • ,
  • Margaret Mitchell
  • ,
  • Aneesh Sood
  • ,
  • Karl Stratos
  • ,
  • Kota Yamaguchi

開始ページ
3562
終了ページ
3569
記述言語
英語
掲載種別
DOI
10.1109/CVPR.2012.6248100
出版者・発行元
IEEE

What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1) factors related to composition, e. g. size, location, 2) factors related to semantics, e. g. category of object or scene, and 3) contextual factors related to the likelihood of attribute-object, or object-scene pairs. We explore these factors using what people describe as a proxy for importance. Finally, we build models to predict what will be described about an image given either known image content, or image content estimated automatically by recognition systems.

Web of Science ® 被引用回数 : 48

リンク情報
DOI
https://doi.org/10.1109/CVPR.2012.6248100
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000309166203092&DestApp=WOS_CPL
URL
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84866726859&origin=inward
ID情報
  • DOI : 10.1109/CVPR.2012.6248100
  • ISSN : 1063-6919
  • SCOPUS ID : 84866726859
  • Web of Science ID : WOS:000309166203092

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