toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Record Links
Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades edit   pdf
url  openurl
  Title Sparse representation over learned dictionary for symbol recognition Type (up) Journal Article
  Year 2016 Publication Signal Processing Abbreviated Journal SP  
  Volume 125 Issue Pages 36-47  
  Keywords Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points  
  Abstract In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.061; 600.077 Approved no  
  Call Number Admin @ si @ DTR2016 Serial 2946  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: