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Author Ivo Everts; Jan van Gemert; Theo Gevers edit  doi
openurl 
  Title Evaluation of Color STIPs for Human Action Recognition Type Conference Article
  Year 2013 Publication IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 2850-2857  
  Keywords  
  Abstract This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF~sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.  
  Address Portland; oregon; June 2013  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue (up) Edition  
  ISSN 1063-6919 ISBN Medium  
  Area Expedition Conference CVPR  
  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ EGG2013 Serial 2364  
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Author Ivo Everts; Jan van Gemert; Theo Gevers edit  doi
isbn  openurl
  Title Per-patch Descriptor Selection using Surface and Scene Properties Type Conference Article
  Year 2012 Publication 12th European Conference on Computer Vision Abbreviated Journal  
  Volume 7577 Issue VI Pages 172-186  
  Keywords  
  Abstract Local image descriptors are generally designed for describing all possible image patches. Such patches may be subject to complex variations in appearance due to incidental object, scene and recording conditions. Because of this, a single-best descriptor for accurate image representation under all conditions does not exist. Therefore, we propose to automatically select from a pool of descriptors the one that is best suitable based on object surface and scene properties. These properties are measured on the fly from a single image patch through a set of attributes. Attributes are input to a classifier which selects the best descriptor. Our experiments on a large dataset of colored object patches show that the proposed selection method outperforms the best single descriptor and a-priori combinations of the descriptor pool.  
  Address Florence, Italy  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue (up) Edition  
  ISSN 0302-9743 ISBN 978-3-642-33782-6 Medium  
  Area Expedition Conference ECCV  
  Notes ALTRES;ISE Approved no  
  Call Number Admin @ si @ EGG2012 Serial 2023  
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